**Energy Saving at Cities**

Printed Edition of the Special Issue Published in *Energies* Francisco Manzano Agugliaro and Alberto Jesús Perea Moreno Edited by

www.mdpi.com/journal/energies

**Energy Saving at Cities**

## **Energy Saving at Cities**

Editors

**Francisco Manzano Agugliaro Alberto Jes ´us Perea Moreno**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Francisco Manzano Agugliaro University of Almeria Spain

Alberto Jesus´ Perea Moreno University of Cordoba Spain

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Energies* (ISSN 1996-1073) (available at: https://www.mdpi.com/journal/energies/special issues/ energy saving cities).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Article Number*, Page Range.

**ISBN 978-3-03936-760-3 (Hbk) ISBN 978-3-03936-761-0 (PDF)**

c 2020 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

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## **Contents**

## **About the Editors**

**Francisco Manzano Agugliaro**, full professor at the Engineering Department in the University of Almeria (Spain), received his M.S. as Agricultural Engineer and Ph.D. in Geomatics at the University of Cordoba (Spain). He has published over 160 papers in JCR journals (https://orcid.org/0000-0002-0085-030X), H-index 26. His main interests are energy, sustainability, scientometrics, water, and engineering. He has served as a Supervisor of 25 Ph.D. Theses; Vice Dean of Engineering Faculty (2001–2004); Director of Central Research Services (2016–2019); Ph.D. Program Coordinator for Environmental Engineering (2000–2012), Greenhouse Technology, Industrial and Environmental Engineering (from 2010); and General Manager of Infrastructures (from 2019) at University of Almeria. Awards: Top reviewers in Cross-Field—September 2019 (Web of Science), 2019 Outstanding Reviewer Award (Energies), 2019 *Sustainability* Best Paper Awards.

**Alberto Jes ´us Perea Moreno**, associate professor at the Department of Applied Physics, Radiology, and Physical Medicine in the University of Cordoba (Spain), received his M.S. as Agricultural Engineer and Ph.D. in Geomatics at the University of Cordoba (Spain). He has published over 40 papers in JCR journals (http://orcid.org/0000-0002-3196-7033), H-index 12. His main interests are renewable energy, energy saving, biomass, sustainability and remote sensing. He served as the secretary of the Applied Physics Department (2017–2019) at University of Cordoba. Awards: 2019 *Sustainability* Best Paper Awards.

## **Preface to "Energy Saving at Cities"**

The use of renewable energies and energy saving, and efficiency are the needs of a global society. According to the latest estimates, global energy demand could triple by 2050, and by then, 70% of the world's population will live in cities. Cities are currently responsible for 80% of greenhouse gas emissions, so they have a key role to play in shifting toward a sustainable energy future. Cities are threatened not only by overcrowding, but also by rising energy demand, obsolete infrastructure, volatile energy markets, and the effects of climate change.This book aims to advance the contribution of energy saving and the use of renewable energies in order to achieve more sustainable cities.

> **Francisco Manzano Agugliaro, Alberto Jes ´us Perea Moreno** *Editors*

### *Editorial* **Energy Saving at Cities**

#### **Alberto-Jesus Perea-Moreno 1 and Francisco Manzano-Agugliaro 2,\***

Received: 3 July 2020; Accepted: 17 July 2020; Published: 22 July 2020

**Abstract:** The use of renewable energies, energy saving, and efficiency are needs of global society. According to the latest estimates, global energy demand could triple by 2050 and, by then, 70% of the world's population will live in cities. Cities are currently responsible for 80% of greenhouse gas emissions, so they have a key role to play in shifting towards a sustainable energy future. Cities are threatened not only by overcrowding, but also by rising energy demand, obsolete infrastructure, volatile energy markets, and the effects of climate change. This Special Issue aims to advance the contribution of energy saving and the use of renewable energies in order to achieve more sustainable cities.

**Keywords:** energy saving; renewable energy; zero-energy buildings; energy efficiency; sustainability; sustainable transport; PV; energy saving in data processing centers

#### **1. Introduction**

Climate change is increasing due to the anthropogenic emission of greenhouse gases. The majority of these are due to the production and consumption of energy. The challenge for the future cities is the implementation of a mechanism that minimizes the need for injection of new energy resources in them, so that a high level of self-sufficiency should be achieved through the concept of circular economy, thus partially mitigating the impacts of climate change. Using solar energy today is considered to be one of the best solutions that can be installed in buildings to help with this issue.

This Special Issue aims to advance the contribution of energy saving and the use of renewable energies in order to achieve more sustainable cities. This Special Issue sought contributions spanning a broad range of topics related, but not limited to:

#### **2. Publication Statistics**

Details of the call for papers for this Special Issue, regarding the articles submitted being published or rejected, were: 10 articles submitted (100%), 3 articles rejected (33.3%), and 7 articles published (66.6%).

The regional distribution of authors by countries for the published articles is presented in Table 1, in which it is possible to observe 20 authors from six countries. Note that it is usual for an item to be signed by more than one author and for authors to collaborate with others from different affiliations. The mean number of authors per published manuscript was four authors.

**Table 1.** Authors' countries.

#### **3. Authors' A**ffi**liations**

This Special Issue's authors and their first affiliations are reflected in Table 2.

**Table 2.** Authors and affiliations.

#### **4. Topics**

Table 3 summarizes the research carried out by identifying the topics to which they belong, according to the proposed topics in the special issue. It was noted that two "Energy Saving at Cities" topics dominated the rest: "Solar Energy", and "Sustainability".

**Table 3.** Energy Saving at Cities.

**Author Contributions:** The authors all made equal contributions to this article. All authors have read and agreed to the published version of the manuscript.

**Conflicts of Interest:** There is no conflict of interest, the authors state.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## **Analysis of Research Topics and Scientific Collaborations in Energy Saving Using Bibliometric Techniques and Community Detection**

**Carmen de la Cruz-Lovera 1,\*, Alberto-Jesus Perea-Moreno 1, José Luis de la Cruz-Fernández 1, Francisco G. Montoya 2, Alfredo Alcayde 2 and Francisco Manzano-Agugliaro 2**

Received: 15 April 2019; Accepted: 24 May 2019; Published: 27 May 2019

**Abstract:** Concern about everything related to energy is increasingly latent in the world and therefore the use of energy saving concepts has been increasing over the past several years. The interest in the subject has allowed a conceptual evolution in the scientific community regarding the understanding of the adequate use of energy. The objective of this work is to determine the contribution made by international institutions to the specialized publications in the area of energy-saving from 1939 to 2018, using Scopus Database API Interface. The methodology followed in this research was to perform a bibliometric analysis of the whole scientific production indexed in Scopus. The world's scientific production has been analysed in the following domains: First the trend over time, types of publications and countries, second, the main subjects and keywords, third, main institutions and their main topics, and fourth, the main journals and proceedings that publish on this topic. Then, these data are presented using community detection algorithms and graph visualization software. With these techniques, it is possible to determine the main areas of research activity as well as to identify the structures of the collaboration network in the field of renewable energy. The results of the work show that the literature in this field have substantially increased during the last 10 years.

**Keywords:** energy-saving; energy conservation; energy utilization; energy efficiency; scientific collaboration

#### **1. Introduction**

The energy problem has become a global issue in a world where science and technology are undergoing a great evolution in a short period of time [1]. Consequently, the development of renewable energy and the use of energy saving concept has been increasing in last years, becoming a research focus around the world [2]. The growing interest in the subject has allowed a conceptual evolution in the scientific community regarding the understanding of the adequate use of energy.

While there is a close relation between the terms "energy saving" and "energy efficiency", there are certain distinctions that need to be clarified. The energetic saving entails a change in the habits of consumption. Sometimes it is enough to eliminate habits that waste energy. On the other hand, energy efficiency is the fact of minimizing the amount of energy needed to satisfy the demand without affecting its quality [3]; it supposes the substitution one machine to another that, with the same benefits, consumes less electricity. Therefore, this does not mean changes in consumption habits. The behaviour of the user remains the same, but less energy is consumed due to the consumption of energy to carry out the same service, which is lower. In Alcott, an extensive review of the literature

on the subject of "sufficiency" can be found [4]. To achieve greater energy sustainability as much as possible, renewable energy and energy saving have been combined [5]. In this way, we will achieve a twofold purpose thanks to a combined strategy of energy saving and energy efficiency, without reducing the quality of life. In fact, it will be maintained, and even increased. These concepts are also making significant advances in the residential construction sectors [6]. Energy saving is not only linked to monetary savings [7], it also contributes to reducing greenhouse gas emissions [8], sustainable development and achieving a sustainable energy supply. However, the reduction of energy demand is not as easy to carry out as is thought and the actions that are imposed are in many cases insufficient to achieve real transformation [9].

Thus, it is necessary to emphasize that, sometimes, many improvements of the energetic efficiency do not reduce the consumption of energy in the amount predicted by the simple engineering models [10]. This is called a rebound effect, since the fact that energy services improve means that they are cheaper and, in turn, that the consumption of these services increases [11]. The Paris Agreement on climate change is an agreement within the framework of the United Nations Framework Convention [12] and energy consumption was the main point to deal with and the creation of an agreement worldwide. To achieve the objectives of this agreement, an outstanding change in the energy sector is necessary, the origin of at least two thirds of greenhouse gas emissions.

The transformation of the electricity sector, with renewable energy, has focused attention on a new debate on the design of the electricity market and electrical safety [13]. However traditional concerns about energy security have not disappeared. In addition, there are also pending issues related to energy access and affordability, global warming and CO2 emissions. Therefore, the massive social concerns can be understood and lead to an increasing demand for ways to improve social and individual energy efficiency.

There remains an urgent demand for ways to improve the energy efficiency of society and people and a need to move increasingly to alternative, low-carbon or non-carbon energy systems. [14]. If we want to achieve sustainable development, energy efficiency becomes a key factor. It is a reality that only a few countries are engaged in research into renewable energy, representing between 70% and 80% of scientific production [15]. In general, countries are in the process of achieving, and in some cases exceeding, many of the objectives set in their commitments under the Paris Agreement. This is enough to reduce the expected increase in global CO2 emissions related to energy, but it is not enough to limit the global warming to less than 2 ◦C. The increase in energy consumption, especially in HVAC systems (heating, ventilation and air conditioning), and CO2 emissions have contributed to more energy policies focusing on the development of energy saving and efficiency strategies as an absolute priority [16]. An example of this is the European Energy Performance of Buildings Directive.

The major advanced economies: USA, the European Union, and Japan appear to be clearly on track to achieve their climate commitments, although it will be vital that these countries introduce other additional improvements in energy efficiency. A clear example of it would be the cement production industry, one of the most polluting. However, several mitigation measures are gaining importance in the recent years, and this industry is increasingly turning towards cleaner production. This is because new alternatives are used as raw materials and fuels. In existing industries, mathematical statistical models, simulation processes, optimization of procedures are everyday methods [17]. Natural gas and oil are going to continue to be the essence of the global energy system for years to come, as governments have indicated in terms of climate. Faced with this situation, the fossil fuel industry should consider the risks of a more direct transition. The interdependence between energy and water will become more pronounced in the upcoming years. In this sense, the management of the relationship between energy and water is crucial for the successful fulfilment of a series of development and climate objectives. It is evident that, to promote energy saving, it should be tried to create efficient energy systems and banish old ones. In fact, there is great potential for energy savings in the renovation of the housing stock in many cities, as is the case in Denmark, where 75% of the buildings were built before the eighties, when the first important demands were introduced for the energy performance of the building [18].

In this way, without altering the development of the technique, significant energy savings will be achieved. Nor should we ignore the human factor; the education and global awareness of citizens is important to achieve energy savings.

Recent studies show how construction technologies have been developed to improve comfort and energy savings in both conventional buildings using concrete or heavy structures, and also in timber buildings. An example of these technologies is the thermal insulation added to the facades and the improvement of the performance of the glazing or a district heating designed in a residential area. Nevertheless, although these technological advances can be implemented in concrete and wood buildings, the studies carried out highlight how timber construction is associated with thermal comfort and is perceived as innovative [19].

This is paradoxical because, after having expanded the use of gravel and concrete, the wood construction was increasingly neglected for a period of time. However, in recent years the Kyoto Protocol has pushed to limit CO2 emissions in all productions. The use of sustainable materials is definitely being stimulated worldwide, as they can store carbon dioxide. The advantages of using sustainable materials have been confirmed by various scientific studies [20].

Buildings using sustainable materials are called green buildings. They reduce and optimize their energy and water consumption as much as possible and integrate into their environment, whether natural or urban, causing the least possible environmental impact. Smaller buildings tend to achieve these objectives satisfactorily. However, in larger green buildings it is much more difficult to meet the requirements of comfort, lighting, noise, use of space, or more hours of operation. It is feared that these innovative buildings will run the risk of repeating past mistakes, especially if they are too difficult to manage [21].

In this process of constructing eco-friendly buildings, it is fundamentally important to know the user's perception of indoor environmental quality and satisfaction in green buildings. Numerous studies show that employees perceive green spaces better than conventional buildings, but those surveyed are not able to differentiate between both types of buildings. However, there is an unequivocal correlation between ecological attitudes among employees of green buildings, showing them to be more satisfied and more prone to sustainable attitudes [22].

In order to achieve appropriate low-carbon rehabilitation interventions, users play a very important role. A sound learning process must be ensured for occupants, designers and building managers. The behavioural habits of citizens are themselves an essential component in reducing consumption patterns [23].

#### **2. Materials and Methods**

The aim of this work is to analyse all the world scientific publications of renewable energy indexed in the Scopus database in order to study the most important areas of investigation in energy saving, as well as to highlight the existing worldwide relationships between researchers and institutions. It is decided to select Elsevier's Scopus for this study as it is the largest database in the world. Thanks to the API interface, it is very easy to automate the search for literature related to the topic by selecting it by authors or by institutions. The two main scientific databases, Scopus and Web of Science pose the question of the comparability and consistency of the results of the different data sets [24]. With respect to journals reported in the two main scientific databases Scopus and Web of Science, a comparative study reveals that the volume of journals on the Web of Science is inferior to the number of journals in Scopus [25], and the relationships obtained in the results with both databanks for the items and the citation numbers by country, and for their rankings, are very robust (R2 ≈ 0.99) [26]. This methodology was successfully used in several scientific fields, so only Scopus data has been used in this research [27].

#### *2.1. Automated Data Collection Applying Scripts*

The flowchart shown in Figure 1 explains how automatic data extraction would be carried out from Scopus. The process that is carried out by Research Network Bot (ResNetBot) [28] could be separated into the following stages: (1) Obtaining information on documents containing 'energy savings' as keywords or included in the title. We must notice that analyse the keywords in scientific articles is one of the most common research topic in different fields of engineering science. This information is represented graphically, where the size of the nodes is proportional to the h index and the lines between two nodes indicate that they are cited. (2) Obtaining information from authors of the works mentioned in step (1), which includes for each author the Scopus author identification number, affiliations, publications and dates, number of citations and h-index. (3) Processed to obtain the collaborative network of authors who have published articles that contain 'energy savings' as keywords or are included in the title. The information stored refers to the number of collaborations between co-authors and their affiliations, country and city. With all this information a graph will be created where the size of the nodes will indicate the scientific production according to the country and will be proportional to the H index and the distance between them will represent the collaboration with one another.

**Figure 1.** Flow diagram showing how the software obtains information from the Scopus database (ResNetBot).

#### *2.2. Data Evaluation and Polishing of Texts*

ResNetBot obtained data that was classified by domains and stored as a compendium of text files in JSON format., http://www.json.org. The Scopus API gives the option of requesting information through all type of details, so we programmed the bot to request all the information available in the files and choose the most convenient data for our work. As a result of the vast volume of data, some irregularities were observed during the work of checking the information. This, however, is a frequent and common problem in these large databases [29]. Phrases with a very similar meaning may sometimes appear written with slight changes. For this reason, it is very important to carry out a debugging process that allows us to obtain the appropriate information. The same expression may not be exactly the same written depending on whether we use capital letters in the first word, the second word, or both. We see an example of this, "Energy saving", "energy Saving", and "Energy Saving".

Therefore, some of the improvement criteria are applied from OpenRefine (http://openrefine.org), which contains some formulae such as clash of keys and neighbourhood algorithms to combine all expressions that correspond to the same idea but are not written in the same way. In order to classify data information and identify singular values, data sheets are finally used; this program has

already been successfully carried out in former studies [30] and could be performed on key-words and author names.

#### Diagnostics and Data Viewing

The data collected by ResNetBot and further processed with OpenRefine was archived in a separate databank. Once this process has been completed, the latter information will be analysed using visualisation tools and statistics based on diagrams.

A graph is a set of vertices(elements) joined by edges. Graphically, the vertices are points and the edges are lines that join them, representing a relationship between two vertices. The visualization of graphs is a great help to determine the interrelation between the different parts. On the other hand, this representation makes it possible to incorporate specific values of the elements and their links. Over the last few years, powerful graphic representation programs have been created that enable the exhaustive analysis of the particularities of the graphics through the use of various functions, such as the reorganization of the vertices and the design of the vertices represented with different colours and sizes depending on their properties. One of the best known when using open source instruments is Gephi [31], which encompasses several statistical tools.

#### **3. Results**

#### *3.1. Energy Saving Publication Relationship*

In Figure 2, the documents obtained in the search described above have been represented all together. The graph represents the relationship between the documents, with each node representing a file. Of the total of 20,095 papers, in the outer halo formed by 12,402 individual papers are represented those that do not cite any other Energy Saving paper, this is 61.7%. Then there is another intermediate halo of 1919 papers that are linked to at least one other paper, but do not connect with the central core, which represent 9.6%. Finally, there is the central nucleus formed by 5774 nodes that are linked together, and that account for 28.7% of the total, is what could be defined as works that scientifically deepen in that field, because they are based on the previous ones to make some type of contribution.

In our research, there are a total of 70,951 keywords. As there is a large number of papers that are not part of the central core, we proceed to a refining of the 5774 core papers. 23,083 unique keywords were obtained, which after the refining process, 10,119 different keywords remain. After this process, the analysis of the documents that are related to each other is done, and they are represented according to the communities detected with the modularity algorithm of Gephi. Once this process is finished, Figure 3 is obtained. Of the 29 communities detected, the 15 main ones, with a percentage higher than 2%, have been represented in colour to distinguish them.

**Figure 2.** Display of the whole energy saving publication relationship indexed by Scopus.

This process has been necessary to reduce the excess of information that would impede the global vision we pursue.

Figure 4 shows the main 15 communities which have been named to facilitate the analysis. This table shows the main keywords of each community detected.

**Figure 3.** Display of the whole energy saving publication relationship indexed by Scopus.

Fifteen communities are detected within the large network of publications related to the term energy saving. In Figure 5, it can be seen that the area with the highest number of publications is lightning and thermal comfort, followed by emission reduction and building insulation. The high energy consumption that occurs in homes and businesses, together with the growing concern about the impact that energy consumption has on the environment, could show how important the search for facilities and systems that enable energy savings in these field is becoming. On the contrary, the railway and public buildings communities are in the last place of the ranking, with a percentage lower than 4%.

#### *Energies* **2019**, *12*, 2030

**Figure 4.** Communities detected for Energy Saving.

**Figure 5.** Communities are detected within the large network of publications related to the term energy saving.

#### *3.2. Authors, A*ffi*liations and Countries in Energy Saving*

Regarding the authors and their relationship among them, it has been seen that in total there are 43,860 researchers. In Figure 6, the authors have been represented by colours according to the country of origin. This figure shows that the researchers that contribute most to the development of scientific production, come from academic and scientific institutions from China, which has the highest number of authors with more than half of the total number, followed by Italy (9.5%), the USA (8.9%), and Japan with 6.8%. Afterwards, with less percentage are Malaysia (4.77%) and Spain (2.2%), and finally a block of countries with a percentage below 2%. Null means that the Scopus register does not contain information on the country of the author.

**Figure 6.** Collaborations between scholars from various countries. The size of the nodes is proportional to the H index.

From Figure 6 it can be drawn as conclusions that among Chinese researchers there is a tendency to collaborate among themselves, but also at the international level with American authors, Malaysians, and to a lesser extent, Japanese authors.

Whereas at European level, the relationship between its authors is with those authors belonging mainly to the European environment. These appear isolated, as can be observed in the lower left part of the graph, where it is extracted that the Italian researchers, the most productive, are mainly connected with the Spanish or with Greek authors among others, but also Japanese and Canadian.

Several layers are observed, being the most external (as explained above) that of the authors who have written about energy saving, but who do not collaborate with anyone in energy saving. Here are 2763 authors, which means a large minority. Then there are intermediate layers, where groups of collaborations are going from less to greater intensity. In this intermediate layer there are 41,097 authors, and it is observed that it is the most numerous. As a result of this international collaboration, many papers have been published.

The method is focused not only on the analysis of the scientific collaborations between institutions of each country, but also to determine which of these institutions are more involved in energy saving.

Researchers associated with Chinese institutions is shown in Figure 7, highlighting the number of people involved in energy savings in the following order: Tsinghua University (2.27%), North China Electric Power University (1.98%), and Tianjin University (1.91%).

**Figure 7.** Connection of authors belonging to Chinese institutions.

In Figure 8 and Table 1 we can see the evolution of the publications developed by the most productive institutions worldwide in the last 10 years (2009–2018). On the bottom of the figure, the production of Ministry of Education of China can be seen, which is the most important institution registered. It does not surprise that, as a fact, it would englobe a lot of researching in the most populated country in the world. This institution represents the 1.64% of the total research (718 publications). Above it, the Tsinghua University represents the 1.59% (699 publications). This institution is one of the biggest universities in Asia with more than 40,000 students, 32,000 of which belong to masters or doctorate degree. After them, we have more institutions and as we can guess, all of them belong to China. It was a highlight surprise to notice all the research developed in this country and the high difference between China and other countries, at least in this search.

**Figure 8.** Ten most productive institutions during the period 2009–2018.

**Table 1.** Number of publications of the ten most productive institutions and universities between 2009–2018.

*3.3. Typology of Literature, Languages of Publications and Distribution of Output in Subject Categories*

Table 2 shows the different types of publications during period 2009–2018. We can see how more than half of the total of publications of the search are articles (50.7%). Following them, the conference papers, with 43.1% of the total. After that, the quantity decreases to different types of documents as Reviews (2.3%), Book Chapter (1.6%), or Conference Revision (1.05%).

**Table 2.** Distribution of the searching documents according to the type of publications between 2009–2018.

The vast majority of articles are published in international journals. Because of that reason, more than 87% of the work have been published in English (31,992), approximately 10% have been published in Chinese (3642) and only 3% includes other languages as Japanese, German, Russian, Polish, or Korean. In Table 3, shows the publications organized by language. Figure 9 shows the classification of publication results by subject, according Scopus.

**Table 3.** Most used languages in documents.

**Figure 9.** Classification of publication results by subject, according Scopus.

#### *3.4. Literature Distribution by Area and Entity*

Figure 10 shows the distribution of publications by country. On the map, red is associated with a high number of publications. Green indicates that there are less publications or grey means that there are no publications or a very limited number of them. It is easily observed how China is the country with more publications (18,309), followed by United States (3716), Japan, and Italy. Also highlight Germany, United Kingdom or Taiwan. This information leads us to the idea of how important the energy saving in the industrialised countries has become, as the richer states have also become involved in this issue by using harsh policies to achieve the proposed objectives. Figure 11 shows the 15 countries that have contributed the most in the last decade.

**Figure 10.** World Map with the 25 countries with a higher number of publications.

It should be taken into account the number of inhabitants of each country in order to properly analyse the results. It is not a big surprise to find out that China is the country with more publications as the population in China is the highest in the world (1,386,000,000 in 2017). This is followed by United States, also with a high population: 327,200,000 at 2018 and Japan with 126,800,000 in 2017. However, it is interesting to find countries such as Italy (60,590,000 in 2017) or Germany (82,790,000) with a lower population but still with a significant number of publications.

In Figure 12, the five keywords that appeared the most in our search are shown. 4/5 of these are related to energy, being the usual investigation and research of this field to create and find new ways of energy optimization and its use in new technologies. We can see how the term "Energy Conservation" started to be used in the beginning of the century and it has increased remarkably from 2013. In the second place, there is the term "Energy utilization" followed by "Energy Efficiency". After them "Energy saving" was very often used from 2008–2010, but it decreased as the researchers tend to focus their work in the conservation of the energy more than the saving of it.

**Figure 11.** 15 countries that have contributed the most in the last decade.

**Figure 12.** 5 most important Keywords and how often they were used during the last decade.

In Figure 13 and Table 4, the ranking of the six most important journals are shown. We have analysed not only the Scimago Journal Rank by Elsevier, but also the CiteScore. The metric of the latter calculates the citations of all papers submitted in the past few years preceding a title. It also includes Source Normalized Impact per Paper, SCImago Journal Rank, quotation and documents counting, and quotation rates. In the figure we have included also the H-Index as it has become very popular in the last decade and the reader can figure it out by the size of the circle according to each of the journals. We can see how "Renew. Sust. Energ. Rev." is the journal with higher not only SJR (3.04), but also CiteScore (10.54).

**Figure 13.** Classification of the first 6 journals. CiteScore is the Scopus ranking and SJR (Scimago Journal Rank) by Elsevier. The size of the circles indicates the H-index.

**Table 4.** Most important journals through the research.

The 21st century is the century of sustainability. This is why the building sector is investing a lot of resources in increasing energy efficiency, concerning both the materials and technologies used to make buildings and building techniques more energy efficient. In other words, it is not only a question of constructing more energy-efficient buildings, such as passivehause buildings and internship rehabilitation, but also of how to construct them, applying more efficient construction techniques, energy savings that must also be extended to the manufacture of construction materials and their transport to the construction site.

In Figure 14 we can see the most important authors related with the search through 2009 to 2018. The most important author is Kansha, who against all odds, is not from China, he is from Japan and has published a total of 57 papers related to the search, representing the 0.16% of the total of publications. He has a total of 1103 citations through all his work and a h-Index of 21. After him, Yang has a total of 47 works (0.13%) related to energy saving. All his work has a total of 8829 citations, getting a 45 H-Index. Other authors such as Li go after with 45 works (0.12%) and the Japanese Tsutsumi with 44 (0.12%).

**Figure 14.** Authors who have published related with the search through the last 10 years.

#### **4. Discussion**

The scientific publications in relation to energy saving have had an exponential growth since the year 2000, from that date until now more than 90% of scientific production is condensed. This indicates that the last 20 years are those that have generated the interest of the States through their researchers and programs to support this research. For example, the research related to energy saving in communication and computation [32]. The works published in the topic of energy saving, are mainly in the form of scientific articles (50.7%), but there is an important number in the form of conference papers (43.1%), which indicates that today it is a novel field of research, in which this topic is addressed in many scientific meetings. It is observed that the books are a percentage below 1%, which means that it is not yet a very well established issue.

Regarding the language of publication, as expected, is dominated by English in more than 87.1%. However, when compared to other scientific fields, it is usual to be above 90%, which suggests that journals in particular countries address this issue in a significant way. As example the Dianli Xitong Zidonghua/Automation of Electric Power Systems, or Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering. Such is the case of China, which contributes almost 10% of all publications in its own language. To a lower extent, German (e.g., the journal eb—Elektrische Bahnen) and Japanese (e.g., the journal IEEJ Transactions on Power and Energy from The Institute of Electrical Engineers of Japan), both around 2%, stand out. In any case, it is clear that China, as one of the world largest consumers of energy, has a great concern for energy saving [33], as was to be expected. This is also evident if it is observed that the main institutions that publish on this subject are also from China, Table 2. If the analysis focuses on the scientific fields where these works are assigned, Figure 9, it is remarkable that the first is not energy, but that the first place is occupied by engineering with 33.1%, showing that developments in this field are the largest part of the contributions, especially those related to energy conservation [34]. On the other hand, it is surprising that Computer Science plays such an important role in this field; this is justified by optimisation-related works [35]. Finally, it should be noted that one of the largest group is "others" with 9.2%. This should indicate the great diversity of scientific fields from which this subject is approached, being important the contribution to the energy saving that is studied from the greater number of points of view [36], which is what will allow to contribute to the global energy saving including geographical areas with serious energy shortages [37].

From the point of view of the networking of the works among themselves, this work shows a vast number of works published on the subject of energy saving, but the most important fact is that about 62% do not cite other work related to this subject. This implies great individualism, and scarce scientific connection in this field, which should make researchers reconsider, because it is

important that research is based on previous studies to progress. However, it may also imply that they are researchers from other fields of knowledge who are becoming aware of or are including these items in their work, which is very encouraging. Within the works that are related to each other and are the core of scientific research in energy saving through the identification of communities have been detected the top 15. In the central positions, we find the scientific fields that might be said are most popular, see Figure 4: Lighting, thermal comfort, building insulation, cooling techniques, HVAC techniques, household-behaviour, or public buildings. Lighting has undergone a revolution since the emergence of solid-state light sources. The efficiency of solid-state technology provides, in a multitude of applications, energy savings, and environmental benefits [38]. Above all energy saving based on lighting has been studied for residential issues [39], public buildings, schools [40], or shopping malls [41]. Another one of the great challenges of energy saving is thermal comfort. Thermal comfort implies a good indoor climate that is important for the success of a building [42], not only by making its occupants feel comfortable, but also by deciding their energy consumption and thus influencing its sustainability [43]. On the other hand, persons have a natural trend to adapting to the conditions changing in their surroundings. This is especially the case with the scientific communities found around the central core of energy saving: building insulation, cooling techniques or HVAC techniques. Therefore, saving energy from households is one of the priorities of most countries, such us China, Italy, USA, or Japan. Those that occupy external positions are considered less integrated with the others, still have great possibilities of integration with the core and are generally the smallest. This is the way to find it: Distillation, communication protocols, electricity market, communication equipment, or railway. Related to these scientific communities it should be mentioned that distillation is still the most popular separation technology of chemical plants, and it uses around half of the operational costs, which shows the major concern of the researchers in applying here the energy saving. The energy savings in the communication protocols has been developed mainly using mobile telephony, where the terminals have a limited battery capacity. The third scientific community found is that related to the electricity market, being this one of variable tariffs, makes energy savings in certain time slots is of special interest. Regarding communication equipment, given the rising digital level of the developed countries, there is a growing concern about the data centres, especially in two respects, cooling and IT load. The amount of world energy consumption has gone from 0.5% in 2000 to 1% in 2005. With respect to the railway, the electric railway system is considered the most environmentally friendly means of locomotion. This is due to two important points: Firstly, the low resistance to running and secondly, the regeneration of energy during braking.

#### **5. Conclusions**

This study presents a wide range of data on the international contribution to scientific knowledge in the field of energy savings during the period from 1939 to 2018. The newness of this work is the application of community detection algorithms which identify scientific communities in the world that develop their work in fields related to energy saving. They also detect the collaboration preferences of their authors at an international level or even at an institutional level as in the case of China. It should be noted that many of the published papers have been the result of this international collaboration.

This methodology makes it possible to understand two complex systems: the network of publications based on quotations and the network of scientific collaborations based on the nationality of the co-authors.

Research Network Bot was used to search for publications indexed by Scopus containing 'energy savings', in the title or keywords. The information obtained was used for the analysis and graphical representation of these networks of scientific collaboration of researchers working in this field.

49,539 documents have been found, from which 55.5% are articles and 38.5% are conference papers. In this period there has been an exponential increase in publications, highlighting the categories of Engineering, Energy, and Computer science.

In light of the data obtained, Chinese institutions have provided an important contribution to the field. It is noted that, logically, the compound terms related with energy are the most used keywords, being energy conservation, energy utilization, energy efficiency, energy saving, and fifthly, optimization, proved as the most popular terms. Different aspects of the publications are analysed, such as language, type of publication, scope, top contributors' countries, institutions, journals, authors, as well as the frequency of appearance of keywords. Geographically, the giants China and United States, followed by Japan and Italy are highlighted as the top contributors' countries. The most active categories within the field of Energy are Lighting with 9.44%, Thermal Comfort (9.18%), Emission reduction and Buildings Insulation, both of then representing more than 8.5%. These 4 fields represent areas where energy saving plays a very important role as buildings, responsible for more than 40% of the energy consumption. In order to reduce this, there should be a release of a kind of regulation or policy to control systems for the installation of lighting.

Therefore, the communities that are in the lead, refer to the state of comfort, but also to the latent concern for the waste emitted into the atmosphere and how these can directly affect our planet in the future. Policies actually adopted in developed countries are insufficient to ensure a significant reduction of the energy consumption. It forecasts that the current global demand for energy in buildings will double by 2050. Thus, it is necessary to implement, at the global level, effective saving policies [44]. The challenge is always to reduce the energy consumption without forgetting the comfort of the user and the requirements of the building. The results of the work show that during the last 10 years have substantially increased the number of publications in this field, which indicates that research in terms of energy saving is assumed as an important and relevant issue in the international scientific scene.

During the last decade China, the most productive country in "energy saving" has become the main leader in the growth of energy efficiency. Its GDP grew a total of 7.8% per year and the energy intensity fell a total of 18.2%. This outstanding result is related with how energy efficiency policies have become the center of the political landscape through these year [45]. Energy efficiency was first included in the five-year plans, in the mid-2000s. It was a first sign of China's acceleration towards greener policies. In addition, energy efficiency has played a role in number of key Chinese economic policies: The market liberalisation and privatisations of the 1990s, the quest for energy security, economic development, or the fight against climate change.

This shows that there is a correlation between the number of publications in this country and the application of energy policies, but we do not have data to extend this assertion to the rest of the countries of the world. The study of the implementation of energy policies in each country, the energy savings achieved and the relationship with its scientific production on the subject, both quantitatively and qualitatively, would be the subject of future work.

**Author Contributions:** J.L.d.l.C.-F. and C.d.l.C.-L. performed the literature review and article writing. A.A. and A.-J.P.-M. analysed the data using community detection methods and article writing. F.M.-A. and F.G.M.: Research idea, article writing, and formatting; they jointly contributed to the structure and aims of the manuscript, paper drafting, editing, and review. All authors have read and approved the final manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*

## **Factors That Contribute to Changes in Local or Municipal GHG Emissions: A Framework Derived from a Systematic Literature Review**

#### **Isabel Azevedo \* and Vítor Leal**

Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI), Faculty of Engineering of the University of Porto (FEUP), Rua Dr. Roberto Frias, 400, 4200-465 Porto, Portugal<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>Tel.: +351-229578710

Received: 15 April 2020; Accepted: 16 June 2020; Published: 19 June 2020

**Abstract:** The changes observed in a municipal or local energy system over a period of time is due a number of concurrent dynamics such as the local social and economic trends, higher-level policies (e.g., national), and the local policies. Thus, a thorough identification and characterization of the many factors of change is key for an adequate assessment of the effectiveness of policies adopted at the local level, as well as for future planning. Such thorough identification and characterization of the factors that are determinant for the evolution of the local energy system is the content of this paper. The identification is performed through a systematic literature review, followed by a synthesis process. The factors identified are grouped into the categories of local context, local socio-economic and cultural evolution, higher-level governance framework, and local climate change mitigation actions. They are represented through a set of measurable variables. The factors and respective variables can be used to improve the disaggregation of changes in the local energy system into individual causes, leading to a better assessment of the evolution over time.

**Keywords:** local climate policy; municipal authorities; municipal energy planning; local GHG emissions; multilevel governance

#### **1. Introduction**

Local actions are crucial for the achievement of global climate change mitigation goals. Both the physical characteristics of the local energy systems and the regulatory competences of local authorities prompt local level as an appropriate level for action [1]. Moreover, local actions are becoming more common, due to the increasingly active role of local authorities and to the recognition of their importance by policy makers at higher governance levels. Thus, there is an increasing need to assess their contribution towards climate change mitigation, and to the achievement of global targets.

However, given the universal scope of climate policy and the link between the local energy system and local context, it is currently very difficult to properly assess the actual contribution of local policy actions. Indeed, the local energy system is influenced by numerous factors, of which only some are related with actions promoted by local authorities. The changes in the economic activity or on population are two examples. The assessment difficulties are amplified by the fact that local policies and actions are implemented as a part of a complex multi-level governance framework.

Previous studies have already highlighted the importance of considering external factors when evaluating local actions. Within the studies reviewed in [2], several factors were identified as key in the link between observed changes and actual effects of local climate change mitigation actions. For instance, the evolution of local socio-economic features (structure of local economy, demographic and economic conditions of local population, etc.), prompted by economic, social, and technological changes, is one of the aspects that are commonly considered relevant for the observed variation in local GHG emissions.

However, within existing studies, a comprehensive analysis of all the relevant factors that influence the local energy system and the corresponding GHG emissions was not found.

Moreover, the analysis of empirical studies focusing on assessing the effects of local actions has emphasized this need to thoroughly identify all the factors that contribute to changes in local GHG emissions [3–5]. For instance, in [3], Millard-Ball performed a quantitative analysis on the effects of city climate policy, without considering any external factors; although some scattered evidence of climate plans effects was found, the findings are not sufficiently robust (which could be partly explained by the non-consideration of other relevant factors). The evidence for impact of local climate plans in the different sectors is not demonstrated with the empirical analysis, which does not provide a coherent result across the different models that were tested. Similarly, in [5], the conclusions drawn were also insufficient to prove the causal link between local actions and GHG emissions. Within the results from the linear regression analysis with panel data, the coefficient associated with the effect of local actions is not statistically significant for any of the countries that were included in the study. However, in the same analysis, the relevance of municipality specificities as well as of higher-level context (i.e., country) for the assessment of local GHG emissions and their evolution over time was confirmed (resulting in coefficients that are statistically significant). Both these studies show the need for a comprehensive consideration of the different factors that impact local GHG emissions to ensure a robust and meaningful assessment of the specific impact of local climate change mitigation actions.

Thus, the identification of the factors of change of local GHG emissions is a crucial step towards a more complete understanding of the observed changes within the local energy system and assessment of the actual effects of local climate change mitigation actions. Indeed, to develop a model that is able to adequately represent the evolution of the local energy system, identifying the links between the changes in local GHG emissions and the different explanatory factors, it is necessary to first identify all the factors that may have an impact on local GHG emissions (factors of change). Herein, factors of change refer to all features and events that lead to changes in the local energy system and respective GHG emissions. These include factors related with local climate change mitigation actions as well as external factors (i.e., factors that are not directly influenced by actions promoted by local authorities). For instance, local demographic evolution—size and structure of local population—may lead to significant changes in the local energy system and, consequently, on the respective GHG emissions.

This paper attempts to perform a systematic identification and characterization of the factors of change of local GHG emissions. This comprises the identification of all factors that may potentially influence local emissions, even if their specific effects in the local energy system are not quantifiable or even relevant (depending on the local system being studied). In order for this to be applicable in the future, it is necessary not only to identify the relevant factors of change, but also to characterize them with indicators. Therefore, that is also covered by this paper. The work is presented in five sections. Section 2 describes the methodological approach that was used to identify the factors of change of local GHG emissions and the review process, including the documents used as reference to this analytical framework, as well as the presentation of the final list of factors identified. Section 3 is dedicated to the presentation of the results, including the description of the identified factors of change and the listing of the respective quantifiable indicators. Section 4 presents a brief discussion of the obtained results, focusing on their potential contribution for improved planning and evaluation of local climate change mitigation actions. The paper ends with some final remarks regarding the results obtained (Section 5).

#### **2. Methodology**

Considering the extensive research that has already been done on this topic, a wide literature review was considered an adequate methodological approach to identify and characterize the factors of change of local GHG emissions. More specifically, the method used consisted in a systematic literature review, using content analysis—which allowed reviewing, assessing, and aggregating a body of literature utilizing pre-specified standardized techniques to all the documents reviewed [6]. The definition of the specifications for data collection and analysis beforehand and respective use as a guide for performing the process aimed to avoid bias in the review.

A systematic literature review process consists of these four sequential steps: (i) definition of the objective; (ii) definition of the specifications to be followed; (iii) the retrieval and assessment of the information; and (iv) synthesis of the results obtained. In what concerns the specifications defined a priori as guidelines for the review, these consist in three types of rules/criteria: (1) the eligibility criteria to retrieve the literature; (2) the rules to retrieve information from literature; and (3) the rules for the analysis and screening of the obtained information. Table 1 presents the details of these three types of criteria.

**Table 1.** Systematic literature review specifications.

The selection of relevant research covered the following keywords: "Energy system characterization"; "Energy system modelling"; "Energy system planning"; "Energy system scenarios"; "Energy policy evaluation"; and "Local climate change mitigation". The keyword selection was based on the different contexts within which this discussion has been raised, including planning, policy evaluation, and energy management.

To guarantee the reliability of the results, the documents considered in the review were restricted to peer-reviewed articles published in scientific journals or conference journals, reports or working papers published by internationally reputed entities (as e.g., International Energy Agency and European Union), and PhD theses. The reviewed documents were published between 1999 and 2017. A list of the documents with a description of their main focus of research is presented in Table 2.

**Table 2.** Characterization of the documents used for the identification of the factors of change.

**Table 2.** *Cont.*

**Table 2.** *Cont.*

Legend: JP, peer-reviewed journal article; WP, working paper; CP, conference paper; Bk, Book; PhD, PhD thesis; PR, policy report; TR, technical report.

The information collected from the documents comprised three types of relevant information: (1) factors of change of GHG emissions; (2) variables that may be helpful to characterize the factors of change identified; and (3) information on the relation between the different factors of change and local GHG emissions.

In regard to the identification of the factors of change, Figure 1 presents a schematic representation of the whole process. The analysis of these documents resulted in a list composed of 511 variables with an impact on GHG emissions. The full list, as well as the reference to the documents where each variable has been referred, is presented in Table A1 (Appendix A).

Then, to reduce the list to the essential, both the eligibility criteria (Box 2 in Figure 1) and the screening filters (Box 3 in Figure 1) were applied successively. The following paragraphs describe this process in detail.

The eligibility criteria were defined to guarantee the applicability of the framework to the assessment of local climate change mitigation actions. Firstly, it was assumed that the respective change must be associated with energy-related GHG emissions, excluding, e.g., the emissions from crops and livestock. Then, the associated effect on the energy system should also be on Scope 1 and 2 emissions—GHG emissions that can be attributed to each municipality (i.e., that local inhabitants can be made accountable for). For instance, with this condition, changes in aviation and international commerce emissions are excluded, since typically an airport serves an area much larger than the municipality. Finally, it was also defined that factors should either change over time and/or influence the effect of the other factors identified. As the goal is to decouple the observed changes in a specific local energy system into effects of the different factors of change, it makes sense that factors that are constant over time (even if determinant for local energy use) must be disregarded. For example, topography of the local territory can have significant implications for local energy use, but it cannot be considered a factor of change as it cannot lead to changes of a specific local energy system over time.

**Figure 1.** Schematic representation of the systematic literature review process.

The implementation of this step corresponded to the exclusion of variables that do not comply with at least one of the three eligibility criteria. The first eligibility criteria led to the exclusion of variables such as the "existence of strategies for landfill methane capture" and "changes in cattle production or fertilization strategies". Moreover, the constraint that implies that each factor must influence local GHG emissions led to the removal of variables such as "level of international trade" and "trade allowances between regions". Lastly, the criteria that exclude variables that do not change over time nor have an impact on policy outcomes implied the exclusion of factors "latitude", "topography", and others. Overall, with the application of the eligibility criteria, 73 variables were excluded from the list. The full list of the non-eligible variables is presented in the Appendix A (Table A2).

Furthermore, a screening process was defined and implemented to reduce the list of identified factors to the essential. This screening process is composed by three main steps that correspond to the application of three distinct filters: (1) explicit repetition; (2) implicit repetition; and (3) redundancy. The screening of the list of variables led to the exclusion of 331 variables. The full list of the excluded variables and the identification of the filters that led to their exclusion are presented in the Appendix A (Tables A3–A5).

The filter of explicit repetition refers to the removal of duplicates, i.e., factors that were identified by more than one document. For example, the number of inhabitants (or overall local population) and GDP were two of the variables that appeared more than once in the list.

Implicit repetition refers to factors that, even if not literally in duplicate, if withdrawn from the list do not imply an information loss. This includes factors that are a combination of other two or more factors that are also listed. For instance, average household area per capita can be considered as implicit repetition if both overall population and built area for residential purposes are already listed. This led to the exclusion of variables such as average household area per person (as total household area and overall population were also listed) and sectorial GDP (as GDP per subsector was also included).

Finally, the redundancy filter corresponds to the screening for dependent factors, whose relation has already been identified by literature. With this filter, for instance, average income per capita was excluded, considering that changes in private consumption (also linked to average income) would imply the exact same impact on local GHG emissions.

Once the variables with an impact on local GHG emissions have been identified and screened, a final list that is consistent with the goal of this work was obtained. The application of the eligibility criteria and the screening filters led to a final list of 107 variables that were grouped into 40 factors of change. This was done through an aggregation process, distinguishing policy-based factors from the ones that occur naturally, and factors related to actions implemented at local level from factors associated with governance actions at higher-levels. This grouping process is detailed in Table 3.

**Table 3.** Final list of variables and respective grouping into factors of change.

**Table 3.** *Cont.*

**Table 3.** *Cont.*

Finally, to complete the theoretical framework, each individual factor was characterized with (preferably quantifiable) indicators. For most of the factors, the documents where they were extracted from also mentioned possible indicators. When more than one indicator was available, the choice fell back on the indicators whose data are more commonly available. When the documents did not mention possible indicators, these were searched for in national statistical agencies and policy reports from national or international governments. Within this step, there was an effort to choose indicators that use data that are commonly available for the municipality level, or equivalent scale, to avoid the need for downscaling.

#### **3. Factors of Change of Local or Municipal GHG Emissions and Respective Characterization**

This section presents the results obtained by the systematic literature review process described in the previous section. This includes discussion detailed description and an analysis of the final list of factors of change that were identified as well as the listing of the characterizing indicators for each factor of change.

To better understand the relation between the different factors of change and prompt a more fulfilling discussion, the 40 factors of change identified in the review were grouped into four distinct sets. Three of these groups correspond to three sources of change: local socio-economic and cultural changes; higher-level governance framework; and local climate change mitigation actions. The fourth group (herein referred to as local context) comprises variables that, despite not leading directly to changes of local GHG emissions, have influence on how the different sources of change impact local GHG emissions (e.g., climate awareness). A schematic representation of the relation between these sources of change and the observed changes in local GHG emissions, considering the local context, is presented in Figure 2.

**Figure 2.** Framework of the sources of change and the observed changes in local GHG emissions.

#### *3.1. Local Context*

Local context refers to the local characteristics that, despite not leading directly to changes on the local energy system, influence the outcomes of both local and higher-level policies. On the one hand, this comprises the local inhabitants' characteristics, as education level, cultural diversity, and awareness towards the problematic of climate change. On the other hand, local context also includes characteristics referring to the local authority as an entity, including human and financial resources as well as legal competences. In general, the characteristics here referred as local context do not change significantly in the medium-term.

A complete list of the factors of change considered as local context is presented in Table 4, along with the respective characterizing indicators.

**Table 4.** *Cont.*

A summarized description of each of the factors of change associated with local context is presented below.

#### 3.1.1. Local Inhabitants' Characteristics

Education level refers to the average level of education of the local population, e.g., whether the majority of local citizens have completed high school, or even a bachelor degree. Here, it was assumed that the distribution of local population per education level could be a good indicator.

Cultural diversity refers to the heterogeneity of local population in terms of cultural background. This is mostly linked with the scale of immigration to the city, currently but also in the last decades. Culture is associated with daily habits and, thus, with specific patterns of energy use and reaction towards certain policy actions/incentives. One of the possibilities to assess this diversity could be through the number of residence permits by reason (or by country of origin).

Willingness to act refers to the level of motivation of the local citizens in having an active role towards climate change mitigation, which is highly associated with their level of awareness to the problem and their civic involvement in the community. For instance, the level of awareness of local population on environmental and climate change issues can have a huge influence on the outcome of any related policy independently on the level of implementation. Even if waste associated emissions are not considered within this work, it is assumed that the share of waste separation could be a good indicator of the local population awareness on climate change issues. Moreover, the degree of civic involvement may be assessed by the number and scale of environmental nonprofits that were created at the local level.

#### 3.1.2. Local Authorities' Characteristics

Legal competences refers to the regulatory competences of local authorities regarding climate change mitigation, i.e., their scope of action. These may vary significantly from country to country and are determinant for the type of actions that are promoted at the local level. The level of autonomy of local authorities can be seen as a good indicator of their scope of action.

Qualified personnel corresponds to the technical competences of the municipal staff for developing and implementing actions targeting climate change mitigation. This can be assessed through the existence of a dedicated agency or municipal department to climate change mitigation, and from past experiences with these issues.

Economic situation translates the economic capacity of the local authority in financing and promoting actions towards climate change mitigation. This can be assessed by the relation between income and expenses of the local authority, which provides a characterization of their yearly profit or debt. Moreover, the ability to obtain funding, through private partnerships or public tenders, is also a relevant indicator.

#### *3.2. Local Socio-Economic and Cultural Changes*

Local socio-economic and cultural changes refer to the group of changes that result from local demographic, social, and economic changes, thus not directly caused by energy-related policies. In planning terms, this would correspond to the "business as usual" or "reference" scenario, i.e., the potential evolution of the system between two distinct moments in time, considering that no policies would be implemented in this timeframe. This comprises demographic, social, and economic changes, as well as climate variability.

The full list of factors of change associated with the local socio-economic and cultural changes that have been identified is presented in Table 5, along with the respective characterizing indicators.

**Table 5.** Factors of change associated with local socio-economic and cultural changes with corresponding indicators.

#### 3.2.1. Demographic Evolution

Demographic changes include the population growth (due to migration and natural growth) and also changes in the age pyramid and in the share of population living in urban and rural areas. All these variations imply changes in the local energy system—in the overall energy use as well as in the structure of this use. For example, energy use in urban areas differ from that in rural areas, due to the differences in heating options available (natural gas vs. traditional wood) and the different household conditions (housing type and size). A summarized description of each of the factors of change associated with demographic changes is presented below.

Population growth refers to the changes regarding the size of local population that occur within the studied timeframe. This is assessed through the number of local inhabitants and their evolution over time.

Migration to urban areas translates the level of urbanization of a municipality and its evolution over time. A change in the level of urbanization has an impact in the local energy system, in terms of preferred transportation modes, average distances traveled, and even in the type of housing. The changes in the level of urbanization can be assessed through the share of local population living in rural/urban areas.

Age pyramid refers to the distribution of the population according to their age. A change in this distribution implies changes in the local energy system, caused by the distinct energy service needs. The differences are more significant between active and inactive citizens. Thus, it is assumed that this factor of change can be assessed by the quantification of the share of active population.

#### 3.2.2. Social Evolution

Social changes refer to the lifestyle changes of local inhabitants that occur naturally, not promoted by energy-related policies. These may include e.g., the increase of appliances ownership per capita and changes in the number of persons per household, in the residential sector, and a change in the average distance between home and work or school, in the transportation sector. Each of the factors of change associated with social evolution are described below.

Household conditions refer to lifestyle changes associated with the residential building. This includes changes in the average number of people per household (usually also linked with the evolution in families' typology) and changes in the average size of the household. These changes can imply significant changes in the energy use for space heating and cooling, lighting, and even cooking. Moreover, changes in appliances ownership must also be considered.

Commuting needs reflect the distance between home and work/school for the local inhabitants. A variation in this distance implies also a change in the daily transportation needs, and often even a change in the transportation modes that are used for commuting. It is assumed that this factor can be assessed through the average distance traveled per person per day, for commuting purposes.

Transportation habits refer to changes in the preferences for daily traveling, for both commuting and leisure purposes. These characteristics may change over time for multiple reasons—economic, social or other. It is assumed that a large share of these changes can be estimated based on the changes in car ownership of local population.

#### 3.2.3. Economic Evolution

Economic evolution refers to the economic situation of local inhabitants and also to the evolution of local economic activities. The effect of these changes on local energy system may be affected, even if not in a very significant way, by local context. For instance, the increase in average income of local population leads to an increase in energy use; however, the growth rate may depend on the level of awareness of the local inhabitants towards climate change issues. The factors of change associated with the economic evolution at the local level are described below.

Overall growth of local economy refers to changes in the level of activity, i.e., the overall value of the local economy. Changes in the local gross domestic products (GDP) are a good indicator for the variation in the level of activity.

Structure of local economy refers to the weight that different economic sectors (or subsectors) represent to the overall local economy. Describing the structure of economy of a specific municipality would consist on identifying the sectors responsible for a larger share of gross value added or for employing more workers and which sectors are less relevant.

Economic situation of households translates the financial situation of individuals. This includes the employment situation, which has a significant impact in the local energy system given the difference in the daily habits between employed and unemployed citizens, and the individuals purchase power. On the latter, there are several studies that demonstrate the relation between private purchase power and energy use.

#### 3.2.4. Climate Variability

Finally, the effects of differences in climate conditions have also been included in the natural evolution of the local energy system. These vary from year to year, and temperature differences lead to changes in energy needs (mostly for heating and cooling), which must be considered. It is assumed that these changes can be assessed through the variations in annual heating and cooling degree days.

#### *3.3. Higher-Level Governance Framework*

Higher-level governance framework refers to the characteristics of international, national, and regional governance frameworks existing within the studied timeframe and focusing on energy and climate issues. Considering the variables identified in the review, it is assumed to be appropriate to separate technology evolution from specific energy and climate policies.

The full list of factors that characterize higher-level governance framework is presented in Table 6, along with the respective characterizing indicators.

**Table 6.** Factors of change related to higher-level governance framework with corresponding indicators.

#### 3.3.1. Technology Evolution

Technology evolution can be prompted by energy related policies (targeting energy efficiency and others) but it may also be market driven (following industry needs or an attempt to reduce costs). The evolution of the energy supply sector can be seen as a mix between natural technology evolution and an effect of energy and climate policies. The energy efficiency improvement on conversion plants is an example of the former, while the preference for renewable energy sources to produce electricity, due to fee-in tariffs in the technological maturation phase, is an example of the latter.

Technical evolution refers to the technology development that occurs naturally, i.e., excluding development that is promoted by energy efficiency policies and other related instruments. This evolution can be assessed by the variation in the energy intensity for each activity sector, at national or even international level.

Market evolution corresponds to the changes in the market that lead to changes in the technology choices by both individuals and companies. This can be translated into changes in technology costs, as well as the presence of market barriers that hamper the shift towards more efficient solutions. The variation in technology costs can be characterized by, e.g., the over cost of A++ appliances compared to a B equivalent. The presence of market barriers and respective evolution over time can be assessed by the quantification of the energy efficiency gap in different moments in time.

Energy supply sector represents the changes in the national and regional energy transformation and transport activities. It includes the evolution in electricity and heat production and transport, as well as the changes in the refinery of fossil fuels. Thus, it comprises the changes in the fuel-mix of electricity and heat generation, i.e., the increase/decrease in the share of renewable energy sources that are used in electricity production, as well as the changes in the efficiency of the energy supply sector (electricity and heat production, but also oil products transformation).

#### 3.3.2. National and International Energy-Related Policies

On what concerns the factors of change associated to energy and climate policies, two distinct groups of factors were found: the ones that characterize the existing energy and climate related policies, e.g., in terms of implementation and specific targets, and the ones that characterize the existing institutional framework regarding the promotion and support of local actions towards climate change mitigation. It is also important to mention the influence that the local context may have on the overall effect of these factors of change on the local energy system. The effects of national and international policies depend on the reaction of targeted actors to the policy instruments put in place. In turn, their reaction depends on their level of education and awareness as well as cultural habits and financial situation. All the identified factors associated with energy and climate policies are briefly described below.

Existing policies correspond to the policies that are implemented at regional, national, and international level that have a climate and/or energy purpose. These may impact positively or negatively local GHG emissions, depending on the coherence between local policies and the ones defined at higher levels of governance. Here, it is assumed that the public and private investments on environmental management and protection could be a good indicator of the scale of existing policies at governance levels other than local.

Energy costs refer to the changes in the average cost of the different energy products and the impact that these may have in the local energy system. Energy prices are usually established at the national and/or regional level, and thus are included within the changes associated with the higher-level governance framework. The average unitary price per energy product in the consumer sector, as well as the respective evolution over time, is considered a good indicator to characterize this factor of change.

Coherence with local policies refers to whether national and regional policy objectives are aligned with those that were defined at the local/municipal level. Here, the quantification of this alignment is not feasible, thus a yes or no question is suggested.

Support of local action refers to the existence of initiatives promoted at national and international level that promote actions taken at the local level. This may include technical support for the development of action plans, and energy dedicated actions, and economic incentives and support (as low-rate loans). A possible indicator to characterize the level of support could be the number of initiatives promoted at higher levels of governance that support local action.

#### *3.4. Local Climate Change Mitigation Actions*

Finally, local climate change mitigation actions include any policy actions that are promoted at the local level, by (or with the support of) local authorities, with the aim of reducing local GHG emissions. These include actions taken under a formal and wider plan and also isolated actions with a very specific target (e.g., the improvement of energy efficiency in public lighting). Here, the factors of change that have been identified are mostly related with the characterization of these actions, in terms of objectives and targeted actors as well as in what concerns implementation procedures. The factors that were identified as important for the characterization of local climate change mitigation actions are briefly described below and presented in Table 7.

**Table 7.** Factors of change associated with local climate change mitigation actions with corresponding indicators.

Actors' involvement refers to the group of actors that were/are involved in the local climate change mitigation actions. A distinction is made between the actors that are involved in the planning process (where actions are defined) and the ones involved in the implementation stage. The involvement of different stakeholders in the planning and implementation of climate and energy actions is considered to be beneficial for the respective acceptance and adherence by local citizens. The characterization of this involvement may be achieved by the identification of the groups of actors that were involved (e.g., local authority, local/regional energy agency, external consultant, local NGOs, citizens, etc.) and their level of involvement (co-creation, draft version public consultation, etc.).

Timeframe refers to the time span of the actions being planned/implemented. Throughout the review, the consideration of the time span of the ex-ante planning exercise (when existing) was also noted as relevant for the assessment of the outcomes of local actions. This is translated into the beginning and end years of the actual (or projected) action implementation.

Targets refer to the objectives that were defined at the local level. These include the level of GHG emissions reductions that is aimed, the wider vision concerning the local energy system, and whether there are other goals besides GHG emissions (such as creation of jobs or decrease in energy costs).

Current implementation status refers to the degree of implementation of the wider action plan (if existing) and of the different individual actions that are projected. It can be assessed as a percentage of the overall implementation target. For instance, considering an action that implied the installation of solar thermal systems for water heating in 100 residential buildings, the degree of implementation would correspond to the identification of the share of systems that were already installed.

Estimated impact corresponds to the projected outcome of the proposed actions, in terms of both energy use and GHG emissions. When a mid-term assessment is also performed, the quantification of the already achieved reductions could also be performed.

Budget refers to the level of investment needed to implement the proposed actions. Here, it is important to distinguish between the overall projected budget and the investments made thus far.

Financing sources correspond to the entities responsible for financing the actions' implementation. This may comprise different entities, both public and private. The characterization of the financing sources could be performed by the identification of the type of entities financing the local actions (local authority's own resources, national funds and programs, EU funds and programs, other), and the level of investment of each of the different entities.

Actions characterization corresponds to the characterization of the individual actions planned and/or implemented at the local level, in terms of: (1) area of intervention; (2) type of technical measure; and (3) policy instrument. Area of intervention is the sector and subsector of the local energy system which will be impacted by the action (residential, services, transports, etc.). Then, the technical measure refers to the type of change that is aimed at, as well as the degree of change that is intended. For instance, the shift of 10% of private cars passenger-kilometers to soft modes of transportation could be seen as a technical measure. Finally, the choice of policy instrument corresponds to the type of mechanism that is used to achieve the desired changes in the system. Using the same example, this could comprise the creation of dedicated bike lanes and/or the implementation of a congestion charge. When assessing the policy instruments, it is also useful to identify the level of financial support that is provided to the adopters of the measures (i.e., if there are economic incentives to change) and which actors are involved in their implementation.

Similar to what was said regarding the effects of higher-level policies, the influence of local context on the contribution of local actions cannot be disregarded. Local inhabitants' characteristics may influence the outcomes of policy actions in a significant manner. Moreover, local authorities' characteristics are an important feature on the definition and implementation of local actions. Thus, even if local actions are similar in their characteristics, their outcome can be very different if the context in which they are implemented is not the same.

#### **4. Discussion**

There are several studies that analyze the changes in local GHG emissions and potential explanatory factors. However, it was noted throughout the review that existing work is usually constrained to the consideration of a specific set of factors of change, disregarding others that may also have a significant impact on local emissions. For instance, it is common to narrow the focus to the actions that are being taken at the local level, disregarding the influence of higher-level governance framework in which they are being implemented and that may have confounding effects.

Such non-consideration of other potential factors of change hinders the accuracy of the results, as the effects calculated and associated with the considered factors may turn out to be either under- or overestimated. Indeed, the evaluation of the effects of local climate change mitigation actions will not lead to accurate results unless the remaining factors of change are taken into account. For example, in [5], the impact of local climate actions was inconclusive, partly due to the non-consideration of factors associated with the changes in local socio-economic and cultural conditions. Moreover, a better understanding of all potential factors of change of local GHG emissions can lead to better planning at local as well as regional and national levels. The interaction between the policies implemented at different governance levels could be improved by a comprehensive consideration of all factors of change and their interactions.

It is worth noting the overly simplified consideration of the local context in most of the existing work. Some of the reviewed documents refer to its importance, and how the local context specificities can be determinant for the success of local actions implementation, yet quantification methods tend to be overlooked. The systematic identification of the factors of change associated with local context may thus be a step forward for their consideration in future empirical ex-post evaluation studies and/or ex-ante planning exercises.

#### **5. Conclusions**

This paper is dedicated to a comprehensive and systematic identification and characterization of the factors that are relevant for the assessment of the observed changes in GHG emissions associated with local energy systems. This is an important contribution for the assessment of the actual effects of local climate change mitigation actions, by establishing the bases for developing analytical frameworks.

The identification was performed through a systematic literature review process. The complete list of variables encountered in the review was reduced using eligibility criteria, as well as three screening filters (explicit repetition, implicit repetition, and dependent variables). A list of 107 variables that were grouped into 40 factors of change was finally obtained.

The factors of change were grouped into four subsets, corresponding to three sources of change plus the local context. The sources of changes subsets are: local socio-economic and cultural changes; higher-level governance framework; and local climate change mitigation actions. The local context corresponds to a group of variables that, despite not leading directly to changes in local GHG emissions, have influence on how the remaining factors impact the local energy system—e.g., local inhabitants' level of awareness and commitment towards environmental problems or local authorities' legal scope of action.

Even if the identification of the major categories has been previously accomplished in academic studies, the major advancement of this work corresponds to their disaggregation into individual factors of change and respective characterization.

The factors identified correspond to all the factors that may potentially lead to changes in local GHG emissions. The relevance of the effects associated with each factor of change will depend on the local system in question, as well as on the timeframe addressed. Moreover, even acknowledging the difficulty of empirical validation (and quantification) of each factor individually, the obtained results comprise an advancement from what was possible thus far, providing a basis for more accurate models and analyses of the evolution of the energy systems at the local level.

These results can be used to build an analytical framework to characterize and model the evolution of a local energy system, assessing the individual effect of each of the different explanatory factors. This will allow for more accurate energy planning at the local level, taking into account the potential interactions with the external factors of change (i.e., the factors that are not controlled by local authorities). For instance, it may be used for ex-post evaluation of local climate change mitigation actions, allowing for the disaggregation of the observed changes in local GHG emissions into the causes and the isolation of the effects that were solely associated to local policies and actions.

The outcome of this work can be seen as a fundamental step towards a comprehensive assessment of the evolution of local energy systems. Such assessment is crucial for a better evaluation of the actual contribution of local climate-related policies and actions towards climate change mitigation.

**Author Contributions:** Conceptualization, I.A. and V.L.; Formal analysis, I.A. and V.L.; Funding acquisition, I.A.; Investigation, I.A.; Methodology, I.A.; Supervision, V.L.; Validation, I.A. and V.L.; Visualization, I.A.; Writing—original draft, I.A.; and Writing—review and editing, I.A. and V.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Fundação para a Ciência e Tecnologia (FCT—Portugal), grant number PD/BD/105862/2014, in the frame of the MIT Portugal Program. INEGI (LAETA—Grupo de Energia e Ambiente) provided financial support with the article processing charges.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

This appendix presents in detail the process of identification and screening of the factors of change that are relevant for local GHG emissions. Table A1 presents the full list of variables identified in the literature. The four following tables are dedicated to the screening process: Table A2 presents the variables that were excluded for non-applicability; Tables A3 and A4 present the variables that were eliminated due to explicit and implicit repetition, respectively; and Table A5 presents the variables that were excluded to avoid dependent variables.

**Table A1.** Full list of variables identified in the systematic literature review process and respective sources.

**Table A1.** *Cont.*

**Document Code Variable** IAEA (2006) 12.040 Electricity penetration for appliances in urban households 12.041 Specific fossil fuel consumption per urban dwelling for lighting and non-electric appliances 12.042 Penetration of various energy forms into space heating in urban households 12.043 Efficiency of various fuels use, relative to that of electricity use, for space heating in urban households 12.044 Coefficient of performance of heat pumps for space heating in urban households 12.045 Penetration of various energy forms into water heating in urban households 12.046 Efficiency of various fuels use, relative to that of electricity use, for water heating in urban households 12.047 Coefficient of performance of heat pumps for water heating in urban households 12.048 Approximate share of water heating demand in urban households that can be met with solar installations 12.049 Penetration of various energy forms into cooking in urban households 12.050 Efficiency of various fuels use, relative to that of electricity, for cooking in urban households 12.051 Approximate share of cooking demand in urban households that can be met with solar installations 12.052 Share of air conditioning demand of urban households that can be met with electricity 12.053 Coefficient of performance of electric air conditioning in urban households 12.054 Coefficient of performance of non-electric air conditioning in urban households 12.055 Fraction of rural dwellings in areas where space heating is required 12.056 Degree days for rural dwellings 12.057 Fraction of rural dwellings per type 12.058 Average size of rural dwellings by type 12.059 Fraction of floor area that is actually heated in rural areas per dwelling type 12.060 Specific heat loss rate by rural dwelling type 12.061 Share of rural dwellings with air conditioning, by dwelling type 12.062 Specific cooling requirements by rural dwelling type 12.063 Specific energy consumption for cooking in rural dwellings 12.064 Share of rural dwellings with hot water facilities 12.065 Specific energy consumption per rural dwelling for electric appliances 12.066 Electricity penetration for appliances in rural households 12.067 Specific fossil fuel consumption per rural dwelling for lighting and non-electric appliances 12.068 Penetration of various energy forms into space heating in rural households 12.069 Efficiency of various fuels use, relative to that of electricity use, for space heating in rural households 12.070 Coefficient of performance of heat pumps for space heating in rural households 12.071 Penetration of various energy forms into water heating in rural households 12.072 Efficiency of various fuels use, relative to that of electricity use, for water heating in rural households 12.073 Coefficient of performance of heat pumps for water heating in rural households 12.074 Approximate share of water heating demand in rural households that can be met with solar installations IAEA (2006) 12.075 Penetration of various energy forms into cooking in rural households 12.076 Efficiency of various fuels use, relative to that of electricity, for cooking in rural households 12.077 Approximate share of cooking demand in rural households that can be met with solar installations

**Document Code Variable** IAEA (2006) 12.078 Share of air conditioning demand of rural households that can be met with electricity 12.079 Coefficient of performance of electric air conditioning in rural households 12.080 Coefficient of performance of non-electric air conditioning in rural households 12.081 Share of service sector in the total active labor force 12.082 Average floor area per employee in the Service sector 12.083 Share of Service sector floor area requiring space heating 12.084 Share of Service sector floor area requiring space heating that is actually heated 12.085 Specific space heat requirements of Service sector floor area 12.086 Share of air-conditioned Service sector floor area 12.087 Specific cooling requirements in the Service sector 12.088 Energy intensity of motor fuel use per Services' subsector 12.089 Energy intensity of electricity specific uses per Services' subsector 12.090 Energy intensity of thermal uses (except space heating) per Services' subsector 12.091 Penetration of various energy forms into space heating in Service sector 12.092 Penetration of various energy forms into other thermal uses in the Service sector 12.093 Efficiency of various fuels use, relative to that of electricity use, for thermal uses in Service sector 12.094 Coefficient of performance of heat pumps in space heating in Service sector 12.095 Share of low-rise buildings in the total Service sector floor area 12.096 Approximate share of thermal uses in the Service sector that can be met by solar installations 12.097 Share of air-conditioning that can be met with electricity 12.098 Coefficient of performance of electric air conditioning in Service sector 12.099 Coefficient of performance of non-electric air conditioning in Service sector IEA (2014) 13.001 Population 13.002 Floor area per capita 13.003 Persons per household 13.004 Appliances ownership per capita 13.005 Heat per floor area 13.006 Domestic how water energy per capita 13.007 Cooking energy per capita 13.008 Electricity for lighting per floor area 13.009 Energy per appliance 13.010 Passenger-kilometers 13.011 Share of total passenger-kilometers by mode 13.012 Energy per passenger-kilometer by mode 13.013 Tons-kilometers 13.014 Share of total ton-kilometers by mode 13.015 Energy per ton-kilometer by mode 13.016 Services value-added 13.017 Energy per value-added (services) 13.018 Industry subsectors value-added 13.019 Share of total value-added by subsector 13.020 Energy per value-added by subsector 13.021 Agriculture value-Added 13.022 Share of total value-added by subsector 13.023 Energy per value-added by subsector 13.024 Changes in CO2 intensity 13.025 Changes in input coefficients 13.026 Changes in the composition of final demand 13.027 Changes in the level of final demand (economic growth)

**Table A1.** *Cont.*

**Table A1.** *Cont.*

**Table A1.** *Cont.*

**Table A1.** *Cont.*

**Table A2.** Variables excluded for non-applicability in the systematic literature review process.

**Table A2.** *Cont.*

**Table A3.** Variables excluded for explicit repetition in the systematic literature review process.

**Table A3.** *Cont.*

**Table A4.** Variables excluded for implicit repetition in the systematic literature review process.

**Table A5.** Variables excluded to avoid the presence of dependent variables in the systematic literature review process.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico**

**Quetzalcoatl Hernandez-Escobedo 1, Javier Garrido 1, Fernando Rueda-Martinez 1, Gerardo Alcalá 2 and Alberto-Jesus Perea-Moreno 3,\***

Received: 14 May 2019; Accepted: 10 June 2019; Published: 18 June 2019

**Abstract:** The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980–2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively.

**Keywords:** wind farm; thermal power plants; peak electricity demand; Gulf of Mexico

#### **1. Introduction**

One of the world's biggest problems is that population always is increasing and some countries have over dependence on energy generation. In the world, primary energy consumption growth averaged 2.2% in 2017, up from 1.2% last year and the fastest since 2013. This compares with the 10-year average of 1.7% per year [1]. In 2016, generation from combustible fuels accounted for 67.3% of total world gross electricity production (of which: 65.1% from fossil fuels; 2.3% from biofuels and waste), hydroelectric plants (including pumped storage) provided 16.6%; nuclear plants 10.4%; geothermal, solar, wind, tide, and other sources 5.6%; and biofuels and waste made up the remaining 2.3% [2]. It is known that energy supply side and demand side is essential to the correct usage of energy [3], and this is important to nations because it depends on the effectiveness of its power systems and the availability of power supply at the load centers [4]. It is important to define the concepts of load and electricity demand, on the one hand to understanding load of an electric power distribution as the final stage of the system that converts electric energy in another form of energy; respecting electricity demand is known as the electric power related on a period of time, that uses load to work.

In Mexico, electricity demand is considered decisive in social and economic development and consequently in the improvement of the economic conditions. The maximum demand for electrical energy occurs mainly during the summer season, between May and September, when the heat is more severe in the north and southeast of the country. As a result, the use of ventilation equipment is increased to maintain the temperature at more comfortable levels and the values necessary for the proper functioning of certain appliances are regulated. In 2017, the energy independence index, which shows the relationship between production and national energy consumption, was equivalent to 0.76. This result implies that the amount of energy produced in the country was 24.0% less than that which was made available in the various consumer activities in the national territory. In the course of the last ten years, this indicator decreased in an average annual rate of 5.0% [5]. An alternative will be the use of renewable energy, the Transition Energetic Law establish a renewable energy minimal participation in electricity generation of 25%, 30% and 35% in 2018, 2021, and 2024, respectively [6]. In the energetic mix, renewable energies have a participation of 14%, the technologies most used are hydraulic, and wind energy contributed with 3% or 38.23 PJ in 2017 [7].

Peak electricity demand is a global policy concern which creates transmission constraints and congestion, and raises the cost of electricity for all end-users [8]. Also, a considerable investment is required to upgrade electricity distribution and transmission infrastructure, and build generation plants to provide power during peak demand periods [9]. This is important because usually service suppliers charge a higher price for services at peak-time than for off-peak time to compensate for the costly electricity generation at peak hours [10]. Thus, if peak demand is reduced, electric system will be benefited. In addition to these benefits a study done by the authors of Reference [11] determine that it can eliminate the need to install expensive extra generation capacity such as combustion turbines for peak hours which are less than a hundred hours a year. With this information regions will be considered where demand complies with the conditions to have the necessary hours. Another benefit is for consumers, because if they know the period of time of peak demand and decrease their consumption, they can avoid higher electricity prices. An option to reduce peak demand is installing peak plants but the economic benefit does not justify the investment. Usually these peak plants use fossil fuel and the result is more generation of greenhouse gases [12].

The use of wind energy to support the electric demand mainly during its peak period is an alternative to reduce fossil fuel combustion and greenhouse gas emissions. Wind is intermittent and uncertain, which represents a problem in power systems [13]. A study done by the authors of Reference [14] established that due to the unpredictable nature of wind energy and non-coincidence between wind units output power and demand peak load, wind units are deemed as an unreliable source of energy. In this study a stochastic mathematical model was developed for the optimal allocation of energy storage units in active distribution networks in order to reduce wind power spillage and load curtailment while managing congestion and voltage deviation.

In New York State the intraregional effects were illustrated by quantifying the net load, net load ramping, operating reserve, and regulation requirements, the study found out that only at wind capacities exceeding 100% of the average statewide load does the wind-generated electricity meet significant portions of the distant demands [15].

An improved energy hub system combined with cooling, heating, and power integrated with photovoltaic and wind turbine, demonstrated that operation cost and carbon emission were reduced by 3.9% and 2.26%, respectively [16]. In Canada a battery storage system to minimize the energy drawn from the public grid was proposed This storage system was charged from wind turbines and the energy stores were discharged to a park when the wind park power dropped below 0 kW and the storage system was able to offset 17.2 MWh but the financial gain was insufficient to offset the net energy losses in the storage system [17].

About cogeneration, in China Reference [18] established the relationship between a wind farm and a thermal power plant, in this study the difference between wind power profits and thermal power is only analyzed in terms of cost differences, and no comparison of other data is involved. According to this, the specific example of Gansu Province not only shows the difference in earnings before and after the peak-to-peak adjustment of wind power, but also indicates that the large-scale auxiliary adjustment of wind power can ensure that thermal power generators can participate in deep-peaking with reasonable returns; Reference [19] analyses and compares several existing kinds of distributed energy storage for improving wind power integration. Then considering a mass of cogeneration units in north China, control strategy for wind power integration is proposed to reduce the peak regulation capacity for wind power integration. As a result, the peak–valley load difference can be equivalently reduced to relieve peaking pressure for wind power integration.

In Northern China a proposal were made suggesting that if the energy carrier for part of the end users space heating is switched from heating water to electricity (e.g., electric heat pumps can provide space heating in the domestic sector), the ratio of electricity to heating water load should be adjusted to optimize the power dispatch between cogeneration units and wind turbines, resulting in fuel conservation, they found out that with the existing infrastructures are made full use of, and no additional ones are required [20]. Bexten et al. [21] investigate a system configuration, which incorporates a heat-driven industrial gas turbine interacting with a wind farm providing volatile renewable power generation. The study quantifies the impact of selected system design parameters on the quality of local wind power system integration that can be achieved with a specific set of parameters. Results show that the investigated system configuration has the ability to significantly increase the level of local wind power integration.

In this study a methodology is proposed, which consists in locating thermal power plants along the five states of the Gulf of Mexico (Tamaulipas, Veracruz, Tabasco, Campeche, and Yucatan) and evaluating wind resources next to these plants with the objective to determine the power output generated and their contribution to reduce the peak electricity demand. For the geographic position of Mexican states along the Gulf of Mexico, see Figure 1.

**Figure 1.** Mexican states along the Gulf of Mexico.

#### **2. Material and Methods**

#### *2.1. Data*

Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides re-analysis data such as: wind speed and wind direction at 50 m height, temperature at 2 m and 10 m, and atmospheric pressure with records from 1980–2018. The resolution of every point downloaded is 50 km covering all the Mexican states along the Gulf of Mexico including offshore points, which can all be observed in Figure 2. Several studies have used MERRA-2 to determine wind characteristics, as did Reference [22] in the Central Californian Coast, assessing offshore conditions. A study about wind and rainfall areas of tropical cyclones making landfall over South Korea was examined for the period 1998–2013 using MERRA-2 data. It was determined that composite analyses of the cases of strong and weak vertical wind shear confirm that the increase of rainfall area is related to the asymmetric convection (rising/sinking motion in the downshear-left/upshear-right side) induced by the vertical wind shear [23]. An investigation on prediction of wind power because of climate change was made using MERRA-2 among other re-analysis data [24]. As can be seen, MERRA-2 data have been used with excellent results on wind assessment.

**Figure 2.** Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data position.

In total, 142 MERRA-2 points, 61 in Tamaulipas, 31 in Veracruz, 9 in Tabasco, 24 in Campeche and 17 in Yucatan.

#### *2.2. Thermal Power Plants*

There are 22 thermal power plants in Mexico that generate electricity. These plants work on a combined cycle with gas turbines. This generation contributes 55.6% of all electricity in the country [25]. Along the Gulf of Mexico, the most important plants are located as follows: two in the state of Veracruz, one called Adolfo Lopez Mateos with capacity of 2263 MW and Dos Bocas with capacity 350 MW. In the state of Tamaulipas there are two, Rio Bravo and Altamira, with capacity of 800 MW and 1039 MW respectively [26]. Figure 3 shows the geographic position of these thermal power plants. Unfortunately, there are no thermal plants located in the states of Tabasco, Campeche, and Yucatan.

**Figure 3.** Thermal power plants.

#### *2.3. Wind Assessment*

Wind characterization is essential to determine its power at a given site. Its assessment depends mainly on the orography, roughness, and generalized climatology.

Statistically it can be characterized as a probability density function (PDF) that can be interpreted as the probability that the random variable *x* lies in a differential range, *dx*, about a value *x* is *f(x)dx*. More specific statements about the probability that the unsteady variable *x*, lies in a particular range *a* ≤ *x* ≥ *b*, this expression is given by Equation (1).

$$P = \left(a \le x \le b\right) = \int\_{a}^{b} f(x)dx,\tag{1}$$

Another PDF used in wind assessment is the Weibull distribution for the wind speed (*u*) that has two parameters: shape parameter (*k*) and scale parameter (*c*). This is expressed in Equation (2):

$$f(u) = \frac{k}{c} \left(\frac{u}{c}\right)^{k-1} e^{-\left(\frac{u}{c}\right)^k},\tag{2}$$

The power in the wind is the product of mass flow rate through turbine blades ρ*UA*, and the kinetic energy per unit mass in the wind, *U2*/2. The average power in the incoming wind is presented in Equation (3) [27].

$$P = \frac{1}{2} \rho A \mathcal{U}^3 \mathcal{C}\_{P'} \tag{3}$$

where ρ is the density of air, *CP* is the power coefficient, *A* is the rotor swept area, *U* is the wind speed and *P* is the wind turbine power output.

#### *2.4. Peak Electrical Demand*

In Mexico the National Energy Control Center (NECC) divided the country in seven regions. This division is called National Interconnected System [28], and is based in two electric regions: Northeast and Eastern [29]. These regions can be seen in Figure 4.

**Figure 4.** Regions analyzed.

NECC shows information about demand forecasting, net demand, and total demand. It uses three methodologies to calculate demand forecasting: moving average that is optimal for random or leveled demand patterns where the impact of historical irregular elements is to be eliminated through an approach of periods of up to 7 days previously. This methodology is the most used by NECC and is given by Equation (4)

$$\hat{X}\_t = \frac{\sum\_{i=1}^n x\_{t-1}}{n},\tag{4}$$

where *X*ˆ*t* is the energy demand average in period *t*; *Xt*−1 is the real demand for the periods prior to *t* and the number of observations *n*.

The second method is weighted moving average, a variation of moving average. While in moving average all data has the same weight, in weighted moving average each data is assigned with a specific weight, always considering that total sum will be 100% and is expressed as Equation (5):

$$\hat{X}\_t = \sum\_{i=1}^n c\_i x\_{t-1} \,\prime \tag{5}$$

where *X*ˆ*t* is the energy demand average in period *t*; *ci* weighted factor; *xt*−1 is the real demand for the periods prior to *t* and the number of observations *n*.

The third method is called multiple linear regression, where both independent and dependent variables working with the independent variable variation to forecasting the dependent variable. Multiple linear regression is given by Equation (6):

$$D\_{\mathbf{F}} = \beta\_0 + \beta\_1 \mathbf{x}\_1 + \dots + \beta\_n \mathbf{x}\_n + \varepsilon,\tag{6}$$

where *DF* is Demand Forecasted; *x*1, ... *x*n independent variables; β*0* ... β*n* coefficients which are calculated by least squares; *n* number of data and ε is a random error term.

Several studies about peak demand where demand consumers participate have established two typical types of programs. The first one is an incentive-based program the end users are encouraged by the incentive payments to reduce their load demands, and thus to help improve the feasibility and stability of the power system [30]. This program usually includes direct load control and demand bidding [31]. The second one is price-based, and in this case final consumers are encouraged to configure their energy usage patterns according to electricity demand curve. These price-based programs consist in observing critical peaking pricing, time of use pricing, and time pricing [32,33].

Figure 5 presents two demand curves, each one by region. These demand curves have been developed with the aim to estimate the current hourly electricity demand profiles with a temporal resolution of 1 h based in 2018.

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**Figure 5.** (**a**) Northeast electricity demand and (**b**) Eastern electricity demand.

As seen in Figure 5, the behavior of electric demand varies among regions, Figure 5a at Northeast the peak appears in range of 16:00–18:00 h and has another peak during 22:00–24:00 h. This is because the consumption increases in the first peak due to domestic use and the second electricity peak is due to industrial sector and in Figure 5b the Eastern region presents a peak demand during 21:00–23:00 h, mainly for industrial activities.

These demand curves will be used and compared to wind daily profile to consider it as wind power.

#### **3. Results and Discussion**

Along the Gulf of Mexico 142 MERRA-2 points have been assessed with wind speed information. 4 sites around thermal power plants have been chosen and the amount of power output generated has been calculated to reduce peak electricity demand.

#### *3.1. Wind Resource Assessment*

Wind has been converted as resource along the Gulf of Mexico; this means that mainly both roughness and orography have been taken into account.

#### 3.1.1. Rio Bravo, Altamira, Tuxpan, and Dos Bocas

Four MERRA-2 points were located near thermal power plants, their geographic information is presented in Table 1.

**Table 1.** Geographic information for Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and thermal power plants.

In Appendix A Tables A1–A4 hourly mean wind speed are presented by month for MERRA-2 points. In each one of them wind speed behavior can be appreciated.

A typical wind year can be compared to electricity demand to identify its trajectory, so that it could be seen if in the peak demand there existed a peak wind. To probe it, Figures 6 and 7 represent both electricity demand and wind speed of Rio Bravo, Altamira, Tuxpan, and Dos Bocas respectively.

As can be observed in Figure 6, wind speed trajectory has an increase in the last hours as the electricity demand does as follows: Figure 6a 6.8 m/s to 7.6 m/s and Figure 6b5m/s to 6.5 m/s both during 21:00 h to 24:00 h. Although Figure 6b does not follow the entire trajectory, at the end of the day the last 4 h (21:00–24:00) when the demand begins to grow, wind also does. In Figure 7a 4.5 m/s to 6.5 m/s and Figure 7b5m/s to 6 m/s both between 20:00 h to 24:00 h. This could be considered to model a potential source of power.

Determining wind speed features correctly means that the model that will be designed will have more accuracy. In Table 2, statistical information at the points studied is presented.

**Figure 6.** (**a**) Northeast demand compared to wind speed at Rio Bravo and (**b**) Northeast demand compared to wind speed at Altamira.

**Figure 7.** (**a**) Eastern electricity demand compared to wind speed at Tuxpan and (**b**) Eastern electricity demand compared to wind speed at Dos Bocas.

**Table 2.** Wind resource statistics.

#### 3.1.2. Microscale Model

One of the critical criteria to place wind turbines is to assess wind resource and determine if the site have features as transmission lines, substations, and roads, among others.

A microscale model has been done on WAsP software including roughness and orography features. In Figures 8 and 9 the four sites are shown microscale modeled. Each one has an array of seven wind turbines placed to avoid wake effect. Wind turbines generators (WTG) are selected according to wind features at the site.

Modeled areas show different types of wind resource, and the resolution is 10 km. The areas of high wind energy potential are better identified. An advantage of microscale modelling is to extrapolate onsite measurements to the prospective turbine sites to modify the background atmospheric conditions and account for topographic induced speedups, turbulence, and wind shear [34]. The *x* and *y* axes are represented in UTM scale, in Figure 8a the zone presents a wind power density between 261 W/m2 to 286 W/m2, WTG type GEWind 77 were placed to avoid wake effects; Figure 8b presents a zone with 170 W/m2, level curves show hills in this area and were harnessed to place WTG type BONUS 600 kW along it. In Figure 9a, this zone is in front of the sea, an area with 142 W/m2, wind turbines VESTAS V82-1650 kW were used; Figure 9b. In Dos Bocas there is a wind power density of 144 W/m2. Seven WTC type BONUS 600 kW were placed at the top of the hills.

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**Figure 8.** (**a**) Rio Bravo and (**b**) Altamira.

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**Figure 9.** *Cont*.

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**Figure 9.** (**a**) Tuxpan and (**b**) Dos Bocas.

3.1.3. Power Output Generation and Electricity Peak Demand

In the northeast region, peak demand occurs twice per day, the first period at 17:00 h and the second one at 24:00 h. These peaks represent a higher price of electricity by consumers. Wind speed at Rio Bravo behaves similarly to electricity demand curve, wind daily profile has two peaks, and these at the same time. Meanwhile, wind speed profile in Altamira only has one peak at the end of the day, even so power can be extracted from the wind. The power contribution by two wind farms placed in Rio Bravo and Altamira in the state of Tamaulipas, respectively, is shown in Table 3.

**Table 3.** Wind farms power generation along the state of Tamaulipas.

In the state of Veracruz located at the eastern electricity region, wind has been analyzed near two thermal power plants. In this region, electricity demand has a peak that occurs at 22:00 h in both Tuxpan and Dos Bocas, and wind speed trajectory shows that it can contribute delivering electricity to the grid at the same time that peak demand occurs. Table 4 presents the power output generated of two wind farms at the state of Veracruz.

**Table 4.** Wind farms power generation along the state of Veracruz.

If the period of time and the wind speed are taken into account when peak demand is increasing, it will be able to calculate how much energy could be extracted in each point, see Table 5.

**Table 5.** Total power generation at peak demand.

As shown in Table 5, electric power generated by each wind farm could contribute to reduce peak demand. The electric power generated by region indicate an amount of 2657.02 MW for Northeast and 3196.18 MW for Eastern. This is the power delivered to the electric grid.

Wind resource assessment is fundamental to determine the site where wind can be used to generated electric power. Another variable that can be considered is the place, which is important taking into account electricity required, as mentioned in Reference [9]. In this work, wind farms were modeled near to thermal power plants and used their electric infrastructure. It was found the hours at day were both electricity demand and wind speed have their peaks, whit this information could benefit end-users as studied [12] that establish if consumers know the period of time were peak demand occurs could avoid higher electricity prices. This work presents a methodology to assess wind speed and calculate the power delivered to the grid, but is important to mention that if only wind speed is assessed and other variables such as roughness, orography, and climatology are not considered, wind cannot be considered as an unreliable source, as did Reference [15]. To avoid this problem, a proposal of cogeneration with thermal power plants has been made, in comparison with Reference [18] where a cost analysis was its main objective, we have analyzed wind data trajectory to reduce peak demand.

The proposal of connection to the power grid is based on the thermal power plant of Dos Bocas. The same connection is considered for the other thermal plants. In this case, the nominal power, that is, 350 MW, and the wind farm nominal power of 10.5 MW, is equivalent to 3% of the total substation capacity which indicates that the connection is possible. For Rio Bravo 800 MW, the connection will represent 1.3%, Altamira 1039 MW will be 1%, and Tuxpan 2263 MW. This connection represents 0.4% of the substation capacity.

Regarding the impact of new wind farms on power grids, Reference [35] established that power losses in distribution systems vary with numerous factors depending on the system configuration, such as level of losses through transmission and distribution lines, transformers, capacitors, and insulator, among others. In the case of this study, all the power losses were calculated in the original design. Appendix B shows a single line diagram where the transmission lines and the node modified with the connected wind farm can be seen.

#### **4. Conclusions**

The microscale model increased the accuracy of wind assessment to place wind farms. This is because when running the microscale model, several variables are taken into account such as roughness, orography, and climatology. In this study, four sites were assessed to model wind farms using MERRA-2 data, wind speed, wind direction, temperature, and atmospheric pressure, with records from 1980–2018.