**1. Introduction**

In the last decade, many modern technologies, solutions, products, etc., have been introduced in various areas of modern man's life, which is, among others things, an implication of implementing the assumptions of the Economy 4.0 project. All of these conceptions change lifestyle, preferences and expectations. This revolution also affected the automotive industry and widely understood transport. The automotive industry is currently undergoing a period of dynamic and permanent change. Over the past decade, the automotive and technology industries, supported by the interdisciplinary efforts of numerous research institutes around the world, have made a significant leap in introducing computerization into what has been purely human activity for over a century, i.e., driving. By introducing functions that seem simple or even basic today, such as anti-lock braking systems and traction control, and gradually introducing more complex functions, such as adaptive cruise control and autonomous parking assistance systems, we are moving closer to the era of unmanned vehicles.

Over the past few years, vehicle manufacturers and technology developers have worked to develop advanced automotive and communication technologies [1,2], modern construction materials, smart vehicle options and alternative types of fuels [3–5]. In addition to the development of engines powered by alternative fuels [5], including those powered by natural gas and hydrogen, we distinguish hybrid and electric vehicles [6,7]. Many car manufacturers work with technology vendors to improve the driving experience, especially in terms of safety and comfort aspects [8]. Hence, these technologies, along

**Citation:** Dudziak, A.; Stoma, M.; Kuranc, A.; Caban, J. Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland. *Energies* **2021**, *14*, 5778. https:// doi.org/10.3390/en14185778

Academic Editors: Guzek Marek, Rafał Jurecki and Wojciech Wach

Received: 31 July 2021 Accepted: 11 September 2021 Published: 14 September 2021

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**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

with the need to meet the needs of an aging population in developed countries, have given rise to the development and gradual implementation of the AV concept and have even made it a necessity and an important business paradigm [9]. It should be mentioned, however, that the first attempt to create driverless vehicles dates back to the early 1920s [10]; however, this concept gained momentum only in the 1980s, when scientists managed to develop automated highway systems [11]. This makes the way for the connection of semiautonomous and AV to the road infrastructure, mainly in Germany and the United States in 1980–2000 [9].

Sensor-based intelligent/autonomous driving systems and advanced vehicle functions generate many potential benefits, the most important of which seems to be the reduction of the number and consequences of accidents (some authors even believe that these systems can virtually eliminate human error, which is a major contributor to road accidents [12]). In addition, multimedia platforms in combination with intelligent and autonomous driving systems can make the time spent in the car, on the one hand, more enjoyable, and on the other hand, more productive, as passengers can multitask while traveling [13]. Advanced communication systems embedded in cars can also lead to more efficient vehicle navigation and traffic flow, reducing congestion and eliminating critical bottlenecks [14]. It should also not be forgotten about reducing energy consumption and pollution.

However, in parallel, there is a discussion about consumer preferences and the willingness to pay for new car technologies and intelligent vehicle options. The speed of market penetration by these technologies, functions and solutions largely depends on whether consumers are interested in these technologies and options and, above all, whether they are willing to pay extra for them. It should be noted that although the idea of self-propelled vehicles without the participation of a driver has existed for decades, too high costs have hindered their mass production so far [15]. In addition, as shown by various studies, many potential users of this type of car have some concerns about their introduction and widespread use, pointing to the existence of several disadvantages, especially in the area of the sense of security, controllability and in the area of law, liability, ethics and cybersecurity.

Taking the above into account, the aim of the study is to analyze the impact of selected factors on the perception of the future of AVs by respondents from Southeastern Poland in terms of comparison with traditional cars. The main goal of the study was to identify the basic factors influencing the opinions of respondents about the social acceptance of AV, in particular the advantages and disadvantages of this solution.

The following research questions were formulated for a fuller implementation of the assumed objectives of the study:

AV technology can and, in principle, should be considered at the meeting point of many disciplines, such as transport sciences, electrical engineering, information technologies, software and hardware engineering, law, and even ethics and philosophy [8].

#### **2. Literature Review**

Currently the automotive industry is experiencing a revolution with the advent of advanced automotive technologies, intelligent vehicle options and alternative fuels. However, research into consumer preferences for this type of automotive technology is very limited. The implementation and penetration of advanced automotive technologies on the market and the planning of possible market adoption scenarios requires the collection and analysis of data on consumer preferences related to these new technologies. Hence, the research by Shin et al. [16] aimed to meet this need by offering a detailed analysis of consumer

preferences for alternative fuel types and technological options, using data collected in the reported experiments conducted on a sample of consumers from six metropolitan cities in South Korea. The results indicated that there is significant heterogeneity in consumer preferences for different smart technology options, such as wireless internet, vehicle connectivity and voice command features, but relatively less diversity in preferences for intelligent in-car applications, such as real-time travel information on parking and road conditions [13].

The use of AVs, as suggested by many authors and experts not only from the automotive industry, generates many different potential benefits, both in an individual aspect-for users of this type of vehicle, as well as social and economic benefits. In relation to the first area, special attention is paid to the fact that AV have the ability to improve passenger comfort by eliminating the need to perform certain tasks related to driving. It is known that the process of driving a car is complex; in fact, several motor and cognitive tasks must be performed, sometimes in quick succession and sometimes simultaneously, with which drivers need to interact; they must also react to different vehicle parameters, driver and pedestrian behavior, all under different weather, lighting and road conditions [17].

In addition, they provide new mobility opportunities for groups of people who, until now, have been partially or completely excluded from participation in public life due to mobility restrictions (e.g., the elderly or disabled) [16,18,19]. The possibility of using AV does not require drivers to have a driving license. It should be added that the lack of the need to drive a vehicle and focus on the conditions and other road users means for some "drivers" more time to work, which in turn is beneficial for the economy.

Taking into account the area of social benefits, it should be emphasized that autonomous driving does not lead to a loss of safety or efficiency of road transport, but rather improves them [20]. It may also result in new business models, as driverless cars will enable car sharing at much higher levels than is currently the case; in the vast majority of cases, it will be cheaper to use these shared cars than your own, without the financial burden and hassle of owning a private car (frozen capital, taxes, insurance, repairs and parking difficulties) [21,22]. The appropriate and adequate information about the number of free places, their location, etc., can reduce even by 30% the traffic volume in some cases [23]. Sharing cars also means a significant reduction in the number of vehicles on the road, as well as less space and infrastructure needed for transport, which in turn means less money spent on it [24,25]. Shared autonomous vehicles (SAV) allow for a shorter travel time and better use of vehicles (reducing their number on roads) and the available urban infrastructure [26]. As shown in Reference [27], the travelers are exposed to a reduction in travel time once conventional transport modes are replaced by AVs. One SAV (shared autonomous vehicle) can replace eight conventional vehicles with acceptable average waiting time ranging from 7 to 10 min and usage of four seats (shared trip) [28].

Ball [29] emphasizes that the benefits of the widespread introduction of AV can be found in two schools [15,30–34]. In the first school, particular attention is paid to the potential of AV to stimulate additional travel. Without the need for a driver, vehicle passengers would have much more freedom to engage in other activities during the journey, which in turn would reduce travel costs and facilitate urban growth. AV would also increase the mobility of younger people (without a driving license), the elderly, the sick or the disabled. It is also emphasized that AV can generate more demand by increasing road capacity, as lanes could be narrowed and cars could come closer together [29].

In turn, the second school emphasizes the potential of AVs to reduce travel/mobility costs. Part of this may be due to the shift from private car ownership towards "mobility as a service" [33]. By replacing the fixed costs of owning a car with fees for access (time and mileage) to a fleet of shared AVs, the final cost of the vehicle's journey would consequently decrease. Moreover, since AVs would not require a parking space close to the destination, this would allow for the further development and filling of the former parking lots [35].

The introduction of AVs would also make it possible to increase road capacity by up to five times [36] and reduce road congestion (and, consequently, fuel consumption and environmental pollution [37,38]), as cars on the network would communicate with each other and local infrastructure to smoothly respond to changes in traffic and flexibly adapt to them [39–42]. Some estimates show that this would save the average commuter 42 h a year [43].

As shown by numerous studies and forecasts, AVs can cause the most intense transformation in urban transport systems and the greatest revolution in the practice of transport planning (proper design of parking lots, streets, networks of transit and paratransit services), since the appearance of the motor car over a hundred years ago [29]. However, attention should also be paid to the strategic implications for other road users-especially for pedestrians. In a risky situation, an AV slows down and gives way to pedestrians—this applies even to unmarked pedestrian crossings. Safer AVs also trigger a rational response from pedestrians and other road users. Pedestrians can pass with impunity with the certainty that the car will stop. The situation is slightly different from the point of view of a passenger in an automated car; as Ball [29] puts it, it would be "like driving a street full of five-year-old unaccompanied children".

No less important is the issue of road safety, which is reflected in the number of road accidents and collisions [15]. It is estimated that AVs can significantly reduce the number of accidents caused by human error, which currently account for over 90% of all road accidents (i.e., driving distracted, e.g., by a mobile phone, speeding, driving under the influence of alcohol/drugs, fatigue and spontaneous decision-making) [44], and consequently drastically reduce the number of fatalities, as well as accelerate emergency response. AVs communicate with each other better than drivers and can more effectively sense the presence of other road users and predict their behavior, as well as prevent them from falling asleep at the wheel [29]. Consequently, self-propelled vehicles can help save lives thanks to advanced avoidance and crash-response technologies. This is because AVs are able to detect the traffic environment, navigate through the software algorithm and control the movement of the vehicle without the driver's decisions and actions [45]. According to data from the World Health Organization (WHO), AVs can eliminate over 1.25 million fatalities from road accidents worldwide [46]. This is all the more important because, according to the WHO, over 1.2 million people die each year as a result of road accidents around the world, and this has a huge impact on health and development [47]. It should be added that road accidents and collisions generate various cost burdens with long-term consequences, affecting productivity, healthcare costs, legal and court costs, losses at the workplace, costs of emergency services, burdens related to congestion, costs of insurance and damaged property.

Moreover, other benefits in the social area include the following [48]:

Attention should also be paid to the potential impact on companies operating directly in the automotive industry, such as taxi services, new car dealerships and repair shops, which will evolve and adapt to the latest technologies. In the study of Berlin by Bischoff et al. [49], the authors examined the replacement of conventional taxis by autonomous taxis; they showed that one autonomous vehicle might replace 10 conventional taxis if the rides are shared, and six if the rides are not shared. From the point of view of the aftermarket, a greater number of AVs may also lead to an increased interest in accessories, such as, for example, advanced video technology providing entertainment to passengers, which in turn will affect the development of companies producing them.

Advanced systems, used in AVs, can also record and share driver data with insurance companies, thus helping to manage claims, estimate insurance premiums and reduce costs. These vehicles can also generate savings in the construction, transportation and logistics industries by providing information on vehicle downtime and cargo status, providing fleet managers with greater operational transparency [50].

Automation of vehicles, despite many potential advantages, may also generate some disadvantages. They can be, among others, issues related to the mentality of the society, such as user's resilience to giving up driving control, loss of situational awareness, loss of driving skills and privacy issues. Equally important are also technical and technological issues, i.e., increased vulnerability to software and hardware defects, as well as the possibility of cybercrime and greater terrorism potential.

Potential users of AVs also point to some understatements in the area of ethics and possible liability for damages; with the advent of the AV concept, a new ethical paradigm appears for man–machine, due to automated risk allocation during collisions.

Other negative aspects of the large-scale introduction of AV technology also include the need to build a completely new legal framework, the need to formulate different approaches to road control and enforcement, the loss of jobs focused on driving, the car navigation system's susceptibility to different types of weather, problems with communication with non-autonomous vehicles in mixed road traffic and the need to make large investments in the current road infrastructure to adapt to the new fully computerized requirements [51].

Other disadvantages of introducing AVs also include their potential negative impact on some enterprises and economies based on public transport, vehicle insurance and services. Insurance companies see the danger of AVs disrupting their business model. Moreover, it was indicated in Reference [38] that reducing the costs of driving a vehicle may increase traffic intensity.

#### **3. Method**

Consumer behavior on the market is a variable category and depends on many factors influencing the decision to choose products and their purchase. One of the most reliable methods of obtaining information on consumer behavior on the market are surveys. The research used in this study was conducted in 2019, among a group of 579 respondents, via the Internet. The original questionnaire was used, with the use of a deliberate sample, as the research concerned only respondents who are drivers or intend to become drivers in the future. The questionnaire used in the research consisted of two parts: the substantive part and the metric questions. The questions contained in the first part concerned many different aspects of the autonomous vehicle market, including the issues of knowledge and acceptance of autonomous vehicles, and opinions on the prospects of their development compared to other types of vehicles, as well as the benefits, disadvantages and barriers and challenges resulting from the introduction of this technology. In turn, the second part, i.e., metric, contained questions enabling the sociodemographic characteristics of the respondents according to various grouping variables (e.g., gender, age, place of residence and driving experience period). The questions in the survey were closed questions: single or multiple choice. In the case of some questions, it was also possible to provide your own answer (if none of the proposed variants of the answer reflected the attitudes of the respondent); however, it was always clearly indicated in the survey.

The results were prepared by taking into account the division of the respondents into four age groups: 19–25 (48% of respondents), 26–40 (24% of respondents), 41–60 (24% of respondents) and over 60 (4% of respondents). They came from the countryside (34% of the respondents) and from cities of various numbers: up to 100,000 (20% of respondents), 100,000–300,000 (10% of respondents) and over 300,000 inhabitants, 35% of respondents). Of the study participants, 38% were women and 62% were men.

In order to achieve the assumed goals, as well as to answer the research questions, the results obtained in the own questionnaire research were statistically analyzed. A correspondence analysis was used, thanks to which it was indicated how the vehicle

market will develop in the near future in the opinion of the respondents, in terms of age and gender, i.e., how respondents from Southeastern Poland perceive the future of AVs in comparison with standard vehicles (SVs). Moreover, the results of research referring to the potential benefits and advantages, as well as disadvantages of introducing AVs onto Polish roads are presented; in order to be able to fully answer the research questions, two variables grouping respondents were used in the analyses, namely gender and age.

The research sample came from the area of Southeastern Poland; this region was selected due to two factors. Firstly, it is an area less prosperous than the western territories and is characterized by a less developed communication infrastructure that can be developed for the use of AVs. Secondly, due to the less developed road infrastructure, it was expected that the social distance to such solutions might be large.

Data analyses were carried out on the basis of the statistical processing software Statistica 13.3 and Excel 2007. In this work, the correspondence analysis method was used. It is a descriptive and exploratory technique for analyzing two-way and multi-way tables, containing certain measures that characterize the relationship between columns and rows. The obtained results provide information similar in nature to the results obtained in the case of factor analysis techniques and allow for the analysis of the structure of the qualitative variables that make up the table. The most common table of this type is the two-dimensional contingency table. In correspondence analysis, the frequencies in the contingency table are first standardized such that the relative frequencies are computed and, when summed across all fields (cells) of the table, they give 1.0. One way to show the goals of a typical analysis is to express relative frequencies in terms of the distance between individual rows or columns in a space with a small number of dimensions.

In addition, the study also used the Spearman rank-order correlation analysis. Spearman's rank correlation coefficient is used to describe the strength of the correlation of two features when features are qualitative, allowing for ordering according to the strength of this feature, or when features are quantitative, but their number is small. In the study, the ranks of the X and Y features were assigned in descending order. If some units of a feature have the same value, then these units are assigned identical ranks, calculating the arithmetic mean of the ranks for the same units.

#### **4. Results**

On the basis of the information obtained from the respondents, in the initial phase of the research procedure, an analysis of correspondence between three groups of characteristics was carried out, i.e., car type (five groups of answers), age of respondents (four groups of answers) and gender (two groups). Respondents were asked about the expected type of the most popular vehicles in Poland in the near future. The following vehicles are included: SV, hybrid vehicle (HV), electric vehicle (EV), AV and standard vehicle supported by autonomous solutions (SV+). In this questionnaire, the respondents did not distinguish between the driving method according to its propulsion (SV petrol or diesel, EV or HV), so vehicles marked as SV, EV, HV and SV+ are considered human-driven vehicles, and an AV is understood as a self-steering vehicle. In order to present the configuration of points representing the input data, a two-dimensional factor space was chosen. The first factor allows us to reproduce 63.01% of input data variation (i.e., total inertia), and the second factor 24.77% (see Table 1).

The largest contribution to the creation of the two-dimensional factor space by the type of vehicle was played by SV+ and SV-coordinate I, and EV and AV-coordinate II. On the other hand, women over 60 years of age (dimension I) and women aged 26–40 (dimension II) had the largest share in the creation of the two-dimensional factor space by age and gender (Figure 1).

**Table 1.** Information resources' factors.

**Figure 1.** Correspondence analysis results between three groups of characteristics—vehicle type, age of respondents and gender.

There are four clear groups of vehicle types with the structure of indicators depending on the sex and age of the respondents (Figure 1), the first is made of EV, the second is SV, the third, with the most average structure, are HV, while the fourth is AV and SV+.

The greatest connection is between men aged 19–25 with SV+ and AV, it is a relatively strong link. Due to the value of the indicator-i.e., the age and sex of the respondents, the group in question differs from other preferred types of vehicle. An equally large link can be noticed between women aged 19–25 and SV+.

SV, on the other hand, correspond best with the group of women and men aged over 60. The age index of these respondents distinguishes this group of cars from the rest.

Another quite strong connection is between women aged 26–40 and men aged 41–60 with the group of EV.

At a later stage of the research procedure, we focused on the analysis of the benefits and advantages and-on the other hand-the disadvantages of using AV in the opinion of their potential users, using two grouping variables-gender and age, in order to identify possible differences in the perception of positive and negative aspects of the AV on Polish roads by women and men in different age groups.

Safer driving is one of the main driving forces in the development of AV and would be a prerequisite for their implementation on public roads in the future. In the research carried out using the proprietary questionnaire, 44% of respondents indicated that if they used AV, they would feel safer. However, most people surveyed are concerned about cybersecurity and the privacy of data about the internet technologies or services they currently use.

Besides safety, the introduction of AV can generate a number of other benefits. Hence, the respondents were asked to indicate other advantages related to this solution. The proposed answers included, among others: comfort (the ability to work and rest while driving, e.g., napping, receiving and writing e-mails, reading a book, etc.), more efficient use of time, greater safety (less accidents and collisions), less stress while driving a car, saving on operating costs, reducing road congestion, a solution for people who do not like driving a car, greater mobility (e.g., disabled, elderly), greater independence (e.g., disabled, elderly or unable to drive), enabling better access to services and more.

The conducted own research indicated gender differences and those resulting from the age of respondents. The results of this analysis are shown for women in Figure 2a and for men in Figure 2b.

**Figure 2.** The advantages of the AV concept in the opinion of women (**a**) and men (**b**), A1—convenient solution, A2—efficient use of time, A3—safety, A4—less stress, A5—economy, A6—less congestion on the road, A7—solution for people who do not like driving a car, A8—mobility and A9—independence.

Young women aged 19–25 most often pointed to the benefits of mobility, independence and comfort; women aged 26–40, apart from mobility and independence, also pointed to the issue of comfort, but also safety (Figure 2a). On the other hand, women aged 41–60 paid attention primarily to safety, but also mobility. In turn, women over 60 years of age clearly indicated less stress while driving and, similarly to other groups, mobility, which can also be understood as greater independence. Interestingly, in all age groups, the least problem, according to the respondents, is access to services and the issue related to the reduction of parking spaces and less congestion on the road.

Compared to women, among the most frequently mentioned benefits related to AV, young men more often perceived comfort, independence and mobility, but also safety (group of men aged 19–25) (Figure 2b). On the other hand, men aged 26–40 most often indicated comfort, mobility and independence; men aged 41–60, apart from the issue of comfort, indicated the issue of effective use of time and safety. On the other hand, men over 60 indicated, similarly to women in this age group, less stress while driving and independence.

Table 2 presents the potential disadvantages of the AV concept perceived by women and men of different age groups. After analyzing the available literature, the proposed answers included boredom while driving; no pleasure in driving; unnecessary driving course; no driving habits; no control over electronics; elimination of many professions, e.g., taxi driver, professional driver, etc.; the threat of cybercrime in the area of vehicles; and others.

**Table 2.** Potential disadvantages of the AV concept in the opinion of women and men.

The conducted research shows (Table 2) that women in the group 19–25 indicated primarily no control over electronics, the potential elimination of professions (e.g., taxi drivers) and the possibility of cybercrimes. Women aged 26–40 spoke in a similar formula, pointing to no control over electronics and the elimination of professions, but at a similar level indicated the risk of cybercrime and the no pleasure in driving a car. On the other hand, women aged 41–60 declared no control over electronics, no driving pleasure, but also the risk of getting used to driving a vehicle. On the other hand, women over 60, among the biggest disadvantages, indicated the lack of driving pleasure, lack of control over electronics and the habit of driving a vehicle.

The research shows that men in the 19–25 age group indicated no pleasure in driving a car and no control over electronics, as well as the potential elimination of professions (e.g., taxi drivers) and the possibility of the so-called cybercrimes. Men aged 26–40 spoke in a similar formula, pointing to the no control over electronics and the risk of cybercrimes. In turn, men aged 41–60 declared no driving pleasure and no control over electronics, but also cybercrimes. On the other hand, men over 60, among the biggest disadvantages, indicated the no driving pleasure and no control over electronics, but also possible boredom while driving a car.

AVs seem to be an interesting solution for many respondents, but not entirely realistic. Table 3 below presents the analysis of the correlation of various variables with regard to associations relating to this type of solution. The performed Spearman correlation, as an analysis, allows us to correlate the variables on the ordinal scale. This is a kind of nonparametric correlation that is based on rank.

Variables: x1, innovative solution; x2, more AV on the road in the future; x3, many different barriers; x4, more positive than negative sides; x5, they still need to be refined; x6, AV is a utopia; x7, not an interesting concept.

> The analysis shows that the correlation of the variables related to the fact that AVs are not interesting (x7), and at the same time, utopia (x6) is of great importance; the second quite strong correlation concerned the relationship between the statement that AVs, despite being an innovative solution (x1), are not interesting for the respondents (x7). Another

correlation is the relationship between more AVs in the future on roads (x2) and more positive sides than negative (x4) and the fact that, although AVs are often treated as utopia (x6), on roads, there will likely be more and more of them on the near future (x2).

Figure 3 shows the approach to the correlation of individual variables presented in Table 3.

**Figure 3.** Three-dimensional sequential graph Spearman rank-order correlations in Workbook1 7v\*7c.

The analysis shows that the respondents are quite cautious in making their judgments about this concept; they are quite skeptical about it, but they are aware that AV will enter the market, and, according to the analyses, most likely not so quickly. As it seems, the AV concept is realistic in the future, but, still, in relation to other types of vehicles traveling on Polish roads, it differs significantly from them in terms of its assumptions, infrastructure preparation and, above all, the mentality and culture of the society.


**1. Introduction**

According to the World Health Organization (WHO), 91% of the world's population lives in areas where air pollution exceeds the safety limits [1]. Air pollution negatively affects both human's daily life, such as emotional and physical health, and sustainable economic growth, such as labor productivity and tourism (e.g., [2–5]). However, air pollutants are not always visible, which might lead to the public being unaware of pollution. Prior studies have indicated that the public perception of air pollution might be inconsistent with objective air quality, which was evaluated using scientific indices such as PM2.5 and PM10 [6]. A potential reason is that individuals' perceived air quality could differ on the basis of their sociodemographic status, including gender, age, education, knowledge, and health status (e.g., [7,8]). Therefore, perhaps there exists a gap between objective and subjective measures of air quality.

There are two important reasons why the relationship between the actual level of air pollution and residents' concern about air pollution should be examined. On the one hand, ignorance or underestimation of the severity of air pollution potentially poses a threat to residents' health since it could increase the probability of long term exposure to air pollutants. For instance, it was reported that 90% of residents in Hong Kong would not stop their outdoor activities in the face of poor air quality [9]. Whether residents' concern about air pollution could correctly reflect the actual pollution level has become a critical question. On the other hand, a high level of public awareness regarding air pollution might contribute to political and social enthusiasm for the enforcement of environmental protection behaviors and policies. Larson [10] reported that the concurrent rise of online platforms in China has become a positive force for environmental data transparency in China. Kay et al. [11] provided further evidence showing that social media are capable of empowering the government to respond to the air pollution problem to ensure social stability. More recently, Lu et al. [12] stressed that public concern about air quality might have a more direct impact than perceived air pollution in influencing people's behaviors and the actions of the community, production sectors, and the government. Therefore, how Chinese residents responded to air pollution should be carefully examined. In particular, more studies are needed to analyze the relationship between the actual level of air pollution and residents' concern about air pollution, given the importance of public concern in shaping public health and environmental regulation practices.

The purpose of this study is to examine whether and to what extent actual air pollution is correlated with residents' concern about air quality in Shanghai, China. Shanghai was chosen as the target city in this study since it is one of the largest and most developed cities in China. Although Shanghai has a high level of economic and social development, its air pollution issue is considerable. According to the Shanghai Environmental Bulletin published by the local government, during the year 2018, air quality was classified as "good" on only 93 days. In 2017 and 2016, the numbers were 58 and 78, respectively. By the end of 2018, Shanghai's air pollution was even reported to be worse than that of Beijing [13], which is well known for its air pollution problem. However, it seems that most attention has been centered on air pollution issues in Beijing in recent years. Public opinion on air pollution from Shanghai residents should be examined since it might maintain pressure on governments to roll out environmental regulations to reduce air pollution effectively and efficiently. From another perspective, understanding Shanghai residents' concern about pollution is valuable in providing residents with useful advice about public health and environmental protection.

In this study, actual air quality was measured by the air quality index (AQI) reported by environmental monitoring stations. Residents' concern about air pollution was measured using the Baidu index for the online search keywords "Shanghai air quality". Baidu index data were provided by Baidu, which is the most popular Internet search engine in China. The index was calculated on the basis of the search volume for a specific search item on a daily basis within a specific region. The Baidu index has been widely used to predict public health issues (e.g., [14–17]) and tourism flows (e.g., [18–20]) in China. Similar to its usage in public health and tourism studies, the Baidu index is also applicable in measuring the degree of public concern about air pollution [12]. This point will be discussed in detail later in the literature review section.

Figure 1 shows the logarithmic values of the actual AQI (blue curve) in Shanghai and the Baidu index (yellow curve) for "Shanghai air quality". The sample period spanned from 2 December 2013 to 31 July 2019, covering 2068 days. The 30-day moving average values of the variables are also displayed to more clearly show varying trends. It can be observed that both the AQI and the Baidu index followed a similar cyclical pattern with apparent fluctuations. The correlation coefficient between logarithmic AQI and the Baidu index was 0.432, indicating a statistically significant positive correlation.

Although Figure 1 provides preliminary visual evidence on the relationship between actual air pollution and residents' concern about air pollution, a basic correlation analysis is not sufficient. To better understand the relationship, the following three research questions are proposed. (1) To what extent and how soon could the actual air pollution level influence public concern about air quality? (2) Does a decline in air quality in another major city, such as Beijing, affect public concern about air quality in Shanghai? (3) Could public concern about air quality and the level of actual air pollution reciprocally influence each other? A vector autoregression (VAR) model was applied to answer these research questions due to its high ability to capture linear interdependencies among multiple variables over time.

It is expected that this study could add to the air pollution literature by examining the reciprocal interactions between public concern about air pollution and the actual degree of air pollution. From a practical perspective, it could help the municipal government of Shanghai better understand the degree of public concern about air quality and better assess the current environmental management practices. This study also provides insights into the spillover effects that the actual air pollution in other major tourist cities in China might have on public concern about air quality in Shanghai.

**Figure 1.** Actual air pollution and residents' concern about air pollution in Shanghai from 2 December 2013 to 31 July 2019.

The rest of this paper is organized as follows. Section 2 presents a literature review and develops the hypotheses. Section 3 describes the empirical model and the data used in the analyses. The estimated results of the empirical model are reported in Section 4. Section 5 discusses the main findings and implications of the results. Section 6 concludes the paper and talks about limitations and directions for future research.

#### **2. Literature Review and Hypothesis Development**

#### *2.1. Air Pollution: Actual Level, Perceived Level, and Public Concern*

Actual air quality levels are measured by scientific techniques, and they are reported in the forms of different air pollutant indicators such as PM2.5 (particulate matter with a size of 2.5 micrometers or less) and SO2 (sulfur dioxide), or comprehensive indices such as AQI and API (air pollution index) constructed using the air pollutant indicators. Generally speaking, there is not much controversy on the proper measurement of actual air quality level, and scientists can measure it objectively and accurately

People's perception of air quality is subjective. Traditionally, researchers have used questionnaire surveys to collect data on perceived air quality level and people's displeasure with air pollution (e.g., [21–23]). Some of the literature reported that perceived and actual levels of air quality are strongly correlated. For example, Atari et al. [24] conducted a community health survey in the "Chemical Valley" in Sarnia, Ontario, Canada, and they observed a significant correlation between odor annoyance scores and modeled ambient pollution. Peng et al. [22] analyzed the data for over 5000 valid respondents in 62 cities, on the basis of the China Social Survey 2013, and reported a congruence of perceived and actual air pollution. Similar findings were reported by some other studies, such as for Northeast England [25], for Switzerland [26], and for China [27].

However, some of the literature found that perceived air quality is not strongly related to actual air quality. For instance, Egondi et al. [21] focused on two slums in Nairobi, Kenya, on the basis of a cross-sectional study of 5317 individuals. They reported that the majority of respondents were exposed to air pollution. However, the perceived air pollution level was low among residents. This implied the need for promoting public awareness on air pollution. Focusing on the city of Wuhan in China, Guo et al. [7] reported that most persons they surveyed believed that air quality had become worse, though statistics of measured air pollutants over the period of 2010–2014 did not actually show that trend. During the period of 2005–2006, Semenza et al. [23] surveyed the residents of two U.S. cities, Portland and Houston. They reported that the residents' perception of poor air quality was not related to PM2.5 or ozone indicators. On the basis of a sample of 200 people interviewed in London in 1999, Williams and Bird [28] reported that public perception of air quality was not a reliable indicator of actual air pollution levels in the investigated areas. The inconsistency between actual and perceived level of air quality was also reported by Berezansky et al. [29] for Haifa, Israel, by Brody et al. [30] for Texas in the U.S., and by Kim et al. [31] for Seoul, South Korea.

A notable recent study was by Lu et al. [12]. They used the correlation-analysis technique to study the relationship between actual PM2.5 concentration and public concern about haze in five large Chinese cities for the period between 2013 and 2017, where public concern was measured by the Baidu index. They reported that short term fluctuations in actual pollution level and concern about pollution were strongly and positively correlated, but long term annual trends of these two variables were opposite.

Overall, the literature has not provided a consensus on whether the public could correctly perceive air quality and whether public concern about air pollution concurs with the actual level of air pollution. There are two possible explanations of this disagreement in the literature. First, different studies were based on different samples. Since social and individual characteristics strongly affect people's viewpoints [31–33], it is natural to see that perceptions of some groups are consistent with actual air quality, but some other groups tend to over- or under-estimate the level of air pollution. Second, public perceptions vary over time. Along with the development of information technology, improvements in education, and the propagation of mass media, the public receives increasing levels of information on the current situation and importance of air quality. Therefore, many people might not have perceived and been concerned with air pollution problems in the past, but they have changed their minds in recent years.

In this study, we focus on the city of Shanghai. Shanghai is one of the most important and developed cities in China. In 2018, Shanghai's GDP per capita exceeded 20,000 U.S. dollars, which was close to that of Greece and Slovakia. Shanghai has a large population size of over 24 million residents, close to the population of a middle sized country such as Australia. As the financial center of China, many companies have their headquarters in Shanghai. Shanghai has good educational resources, including 64 universities. As an international city, Shanghai is visited by approximately nine million (person-times) inbound tourists annually. Therefore, on average, Shanghai residents have the

knowledge and ability to perceive the importance of clean air and to worry about pollution if they feel that air quality has declined. Accordingly, the first research hypothesis in this study was established:

#### **Hypothesis 1.** *Deprivation of air quality in a local area raises the public concern about the air pollution problem.*

Furthermore, since the Internet has allowed information to flow swiftly across regions, news about air pollution in one major city could quickly propagate and might cause mental or even physical responses of residents in another city. In this study, Beijing, the capital of China, was used as a study area because it is notorious for its severe air pollution (it is also important to note that other major tourist cities, including Nanjing and Guangzhou, are also examined in the robustness check section). If Beijing's AQI rose to an unhealthily high value, the residents in Shanghai might also want to check the air quality in their local area after they receive the information about Beijing. Thus, the second research hypothesis was built:

**Hypothesis 2.** *Deprivation of air quality in another major city raises public concern about the air pollution problem in the local city.*

#### *2.2. Importance of Air Pollution Awareness and Concern*

People's concern about air pollution is largely due to the health risks it has [21,34,35]. People's concern about air quality promotes people's avoidance of the polluted environment. For instance, people tend to limit their social activities on polluted days [36]; people are unwilling to buy houses located in polluted areas [29,37,38]; and tourists flow from polluted to less polluted cities [39,40].

Public concern about air pollution generates pressure on the government to implement policies to promote better air quality. Environmental perception and concern also promote more pro-environmental behaviors [41,42]. Tam and Chan [43] further examined the association between environmental concern and behavior across 32 countries (China was not included), and they indicated that the association was weaker in countries with stronger distrust and belief in external control. To better understand the strength of this association in the case of Shanghai, the third research hypothesis was built:

**Hypothesis 3.** *Public concern about the air pollution problem pushes people or governments to take actions to reduce air pollution.*

#### *2.3. Applicability of the Baidu Search Index to Measure Residents' Concern about Air Pollution*

Internet search engines enable quick access to information. Do et al. [44] indicated that tracking online search behavior using relative search volumes was an effective way to gauge public interest. A number of researchers have used the search index as a proxy of issue salience or attention in the public. The majority of search-volume data are extracted from two dominant engines, Google and Baidu. For example, Caputi et al. [45] noticed a significant public interest in heat-not-burn tobacco products on the basis of Google trends data. Do et al. [44] utilized Google trends data to assess public awareness on protected wetlands in South Korea. Mellon [46] found that Google trends data could be used to measure the salience of four issues (fuel prices, economy, immigration, and terrorism) in the United States. Search index data have also been used to predict tourism flows (e.g., [18–20,47]).

Given the fact that the Baidu search engine occupies a dominant market share in China, the Baidu search index has become a useful proxy for Chinese residents' interest and concerns. In Li et al. [16], Baidu search query data were found to be a reliable indicator for monitoring and predicting the HIV/AIDS epidemic in China. Liu et al. [48] used the Baidu index to assess people's awareness of avian influenza A(H7N9) in Zhejiang, China. Using the Baidu index, Yang et al. [49] found that the daily average PM2.5 concentration had a weak impact on public awareness of lung cancer risk in China. Lu et al. [12] used the Baidu index to measure public concern about haze in five large Chinese cities. Given that the use of the search index has been extensively applied in multiple disciplines as a method for surveillance, monitoring, and measuring interest on and concern about specific topics, this study adopted Baidu index data to measure residents' concern about air pollution in China.