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Journal of Resources and Ecology >

2023 , Vol. 14 >Issue 3: 502 - 516

DOI: https://doi.org/10.5814/j.issn.1674-764x.2023.03.007

Ecosystem Services and Sustainable Development

Market-incentive Environmental Regulation and Urban Resilience: Heterogeneity and Influence Mechanisms

  • LIU Yaqin , * ,
  • LI Min
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  • School of Economics, Shanxi University of Finance and Economics, Taiyuan 030006, China
*LIU Yaqin, E-mail:

Received date: 2022-01-10

  Accepted date: 2022-07-20

  Online published: 2023-04-21

Supported by

The National Natural Science Foundation of China Youth Project(72103113)

The Humanities and Social Science Fund of Ministry of Education of China(21YJA630071)

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Abstract

The market-incentive emission trading system is an important element for improving urban governance. The main objective of this study was to examine the hypothesis that market-incentive environmental regulations have an impact on urban resilience. The entropy method was used to construct six dimension-specific objectives to comprehensively portray the level of urban resilience, and then the double difference method and the moderation model were used to investigate the impact of market-incentive environmental regulation on urban resilience and its mechanisms. The results show that up to two-thirds of cities are at a low resilience level. Second, the emission trading system significantly enhances the resilience of cities over time. Moreover, the effects of energy saving and emission reduction, marketization level and innovation vitality are important mechanisms for improving the resilience of cities. Furthermore, the lower the degree of green development of the city itself, the more significant the effect of the emission trading system on improving the resilience of the city. For different types of resource-based cities, the enhancement effects on urban resilience are growth cities, regeneration cities, mature cities and declining cities in descending order. To improve the level of urban resilience, it is necessary to release the policy dividend of regional coordination and to deepen development according to regional endowment differences. Finally, the findings of this analysis can provide some theoretical support and experience reference for deepening the market-incentive reform of environmental governance and promoting the high-quality development of resilient cities.

Key words： urban resilience; environmental regulation; DID multiplier method; moderation model; urban heterogeneity

Cite this article

LIU Yaqin , LI Min . Market-incentive Environmental Regulation and Urban Resilience: Heterogeneity and Influence Mechanisms[J]. Journal of Resources and Ecology, 2023 , 14(3) : 502 -516 . DOI: 10.5814/j.issn.1674-764x.2023.03.007

1 Introduction

In recent years, new and compound urban disasters such as urban flooding, extreme weather, traffic congestion, and the COVID-19 epidemic have become normalized and more destructive. The sensitivity and vulnerability revealed in the urbanization process have been intertwined and overlapping, which makes it urgent to implement the concept of urban security development, and promote high-quality urban development and resilience.
Western countries, such as the United States, Australia, and the United Kingdom, were the first to study urban resilience, mainly focusing on the connotation of resilience (Ahern, 2011), resilience evaluation (Sharifi and Yamagata, 2016), urban planning (Chen et al., 2017), evolution (Scheffe, 2009) and the resilience of a certain sub-function, such as economy (Shutters et al., 2015), society (Kirmayer et al., 2009), ecology (Alberti and Marzluff, 2004) and infrastructure (Todini, 2000). Based on this background, Chinese scholars have conducted qualitative and quantitative studies on urban resilience from different perspectives. According to the existing literature, the early studies mainly focused on the evaluation method of urban resilience and the comparisons of temporal and spatial characteristics. At the prefecture-level city level, some scholars portrayed the spatio-temporal differentiation of urban resilience based on the three-dimensional scale-density-morphology resilience analysis framework (Wang et al., 2021) and the α and β convergence method (Zhang and Li, 2020). From the perspective of urban agglomeration, many scholars analyzed the spatio-temporal pattern evolution of urban cluster resilience through comprehensive evaluation methods, such as the urban network structure resilience evolution evaluation model (Xie et al., 2020), ecological-economic-social-engineering assessment framework (Chen et al., 2020a), entropy method (Zhu and Sun, 2020) and barrier degree model (Chen and Xia, 2020), and explored the factors influencing the spatial-temporal characteristics of urban resilience. In addition, many studies were conducted on building resilient cities from the perspective of disasters. For example, based on the perspectives of sea-level rise (Chen et al., 2020b), flooding (Zhang et al., 2020a), tropical cyclones (Du et al., 2020), and fire prevention (Yan et al., 2020a), these studies examined how to enhance urban resilience in a targeted way, so as to improve the ability of the cities to cope with disaster risks. In recent years, many scholars have incorporated ecology, sociology, and economics into the study of urban resilience, which has greatly enriched the connotation of urban resilience. Aiming at the current trend of China’s economic transformation towards high-quality development, a large number of scholars are beginning to study the resilience of the urban economy, and there are two main research routes. One is the measurement of economic resilience (Feng et al., 2020b), and the other is to identify the factors influencing urban economic resilience based on the empirical data of Chinese cities. These studies have found that the mechanisms enhancing urban economic resilience are in the areas of economic agglomeration (Zhao and Wang, 2021), technological spillover (Chen and Wu,2020), entrepreneurial dynamism (Su and Zhao, 2020), and diversification and innovation capacity (Xu and Deng, 2020). The positive spillover effects of industrial diversification (Liu and Li, 2021), economic development, environmental pressures (Feng et al., 2020a), and urbanization levels (Zhou and Liu, 2020) on urban resilience also were identified in some recent literature. However, few scholars have paid attention to the ways to improve urban resilience under environmental regulation.
At the same time, China has long been committed to deepening the reform of its ecological civilization system and promoting the transformation of urban green resilience. Theoretically, reasonable and strict environmental regulation policies can internalize the negative externalities of pollution emissions, especially market-incentive environmental regulations that can more efficiently and flexibly achieve incremental improvements in green technology innovation (Tao et al., 2021). This has become an important way to enhance the ecological resilience of cities and improve the human living environment. In the process of rapidly improving the resilience of cities, fully unleashing the boosting power of market-incentive environmental regulation has become a major priority for the overall improvement of urban quality under the new development pattern.
Since market-incentive environmental regulation has been implemented for more than 40 years, previous studies have discussed the effects of environmental regulation on pollution control and emissions reduction, and achieved valuable research results. However, there is little research on the mechanisms driving the impacts of market-incentive environmental regulation on urban resilience. Only some related literature has focused on how market-incentive environmental regulations affect urban resilience. Ma and Hu (2019) argued that market-incentive environmental regulations are endogenous to the adoption of green behavior by firms. Many scholars have argued that market-incentive environmental regulations promote the green development of industries (Zhang et al., 2020b) and an increase in the green total factor productivity (Wen and Zhou, 2019). Some scholars also suggest that the green effect of market-incentive environmental regulation is phased. For example, Yan et al. (2020b) argued that market-incentive environmental regulation inhibits in the short term but positively promotes the development of green industry in the long term. In addition, some scholars have studied the sub-topics of technological innovation (Hu et al., 2020), green technological innovation (Wang and Zhang, 2018; Fan and Sun, 2020), industrial structure upgrading (Sun et al., 2018), and the income distribution of urban residents (He, 2019). These studies provide new perspectives and ideas for this investigation.
The contributions of this study are mainly in the following three aspects. Firstly, in order to effectively measure the level of urban resilience, it is necessary to build a richer and more accurate six-dimensional urban resilience evaluation index system. Therefore, based on the four dimensions of urban resilience: technical, organizational, social and economic, this study constructs a more comprehensive and accurate six-dimensional urban resilience evaluation index system to portray the level of resilience of Chinese cities by using the “multi-criteria integration-single indicator quantification” method. Secondly, this study enriches the theoretical literature on the relationship between urban resilience and environmental regulation, and also enriches the research on the degree of green development as an urban research perspective. Thirdly, the findings of this study not only make up for the lack of research in this field, but also provide some scientific basis and experience for deepening the reform of ecological and environmental governance and for promoting the construction of new cities.

2 Institutional background and theoretical analysis

2.1 Institutional background

As one type of environmental regulation, market-incentive environmental regulation has played an important role in the process of environmental governance in China. Since the sewage charge system was implemented in 1979, China’s environmental governance has gradually evolved from predominantly command-based environmental regulation to a mix of market-incentive-based environmental regulation. Compared with command-based instruments, market-incentive environmental regulations are more efficient and flexible (Jaffe and Stavins, 1995) and the policy effects of energy conservation and emission reduction are greater (Wang and Qi, 2016). Market-incentive environmental regulation has mainly gone through three stages. In the first stage (1979-2006), the sewage charge system pilot emissions trading for sulfur dioxide was launched in Shandong and Shanxi in 2002. In the second stage (2007-2012), the pilot emissions trading system was further expanded in 2007, with the National Development and Reform Commission approving of 11 provinces (Tianjin, Hebei, Shanxi, Inner Mongolia, Jiangsu, Zhejiang, Henan, Hubei, Hunan, Chongqing, and Shaanxi) as the pilots and specifying that the emissions targets for all pilot areas included sulfur dioxide, while the green financial system began to take shape. In the third stage (from 2013 to the present), the carbon trading pilot system and environmental tax were implemented, the green credit system took shape, and the national carbon trading system was officially launched in 2017. Thus, China’s environmental governance system has initially formed a new market-incentive pattern with “effective market mechanisms, dynamic micro-entities, and moderate macro-control”.
On the whole, as the longest-running and most influential market-incentive environmental regulation instrument, the emissions trading system has become an effective way to achieve the two carbon goals and an important focus for enhancing urban resilience. Therefore, this study selected it as the characteristic policy of market incentive environmental regulation.

2.2 Mechanisms of the impacts of emissions trading systems on urban resilience

This study considers three important mechanisms as the reasons why emissions trading systems can influence urban resilience.
Firstly, the emissions trading system improves the ecological environment. According to the system, a maximum limit of pollutants emitted in a certain region is set, and the emission quota is regarded as an asset and traded through the market which can significantly contribute to pollution emission reduction and thus enhance the ability to resist disasters, i.e., the robustness. At the enterprise level, the implementation of the emissions trading system has obvious advantages in encouraging enterprises to reduce both pollution (Shen and Yang, 2017) and SO2 emissions (Zhang et al., 2017). At the national pilot level, it has effectively curbed pollution emissions in the pilot provinces (Qi and Chen, 2020), and Liu (2019) also found that there is geographical heterogeneity in the effect of the emission reductions. In addition, Wu and Ge (2018) pointed out that the emissions trading system can achieve both environmental and economic effects, i.e., double dividends. In other words, on the one hand, the pollution abatement effect caused by the emissions trading system improves the local ecological environment. On the other hand, it enhances the ecological resilience of cities by positively moderating the dividend effect of environmental regulation.
Secondly, the emissions trading system accelerates the upgrading of industrial structures. The emissions trading system is essentially a social application of the Coase theorem. The government defines the initial ownership of emissions rights, and then reduces the transaction costs through the market. As the marginal cost of pollution control is unequal among the various emitters, the one with the lower cost is more inclined to increase its investment in pollution control and obtain the benefits of emission reduction. However, the party with high pollution control costs chooses to purchase emission rights to reduce the cost of environmental control, which is an external uneconomical cost. Emissions trading internalizes the external effects, which helps to optimize the efficiency of resource allocation and avoids the “tragedy of the commons”. There is consistency among the environmental, economic, and social benefits. The construction of an open and efficient emissions trading market not only helps to ensure the smooth realization of the trades, but also promotes the transformation and upgrading of the city’s industrial structure, which becomes a prerequisite for enabling the emissions trading system to enhance the redundancy of the city. In fact, given the traditional factors, the level of marketization has a positive effect on the level of economic growth (Wang and Zhou, 2015) and the quality of economic development in the cities (Sun, 2020). Furthermore, marketization has positive moderating effects on environmental regulation to improve total factor productivity (Fan et al., 2011) and on emissions trading systems to reduce energy consumption intensity (Shi and Li, 2020). In short, the improvement of marketization can promote high-quality development on the one hand. On the other hand, it can also have a positive impact on the effect of environmental regulation policies, thus achieving the effect of enhancing urban resilience.
Thirdly, the emissions trading system stimulates innovation and vitality. As one of the market-incentive environmental regulations, the emissions trading system is reasonably designed, flexible, and free, which puts pressure on enterprises to reduce emissions and prompts them to increase their R&D and innovation expenditures, thus obtaining innovation compensation and triggering the Porter effect (Porter and Linde, 1995). After the implementation of the emissions trading system, on the one hand, enterprises reduce the pressure of environmental regulation by improving pollution control technologies or increasing total factor productivity, which triggers the Porter effect (Wu and Ge, 2018). On the other hand, it positively regulates the promotion of green development by the emissions trading system (Zhou and Fang, 2019). In summary, the emissions trading system provides a good endogenous engine for urban development, which leads to cities that show high-quality development.
Based on the above analysis, this study puts forward the following three hypotheses.
Hypothesis 1: The effectiveness of energy saving and emission reduction plays a positive moderating role in the relationship between the emissions trading system and urban resilience.
Hypothesis 2: An emissions trading system promotes industrial upgrading through increased marketization, which in turn enhances urban redundancy.
Hypothesis 3: An emissions trading system helps to stimulate innovation which enhances the wisdom and adaptability of cities.

2.3 Heterogeneous analysis

2.3.1 Differences in the construction of green development in cities

When testing the impact of market-incentive environmental regulation on urban resilience, it is necessary to consider whether there is heterogeneity due to the level of green development of the cities. Compared to cities with a low level of green development, cities with a high level have a significantly optimized industrial structure, a continuously improved level of clean production and a green orientation in their production and lifestyle. Usually, citizens are more proactive and motivated to protect the ecological environment, thus cities with a high level of green development are in a higher level of urban resilience. It is therefore hypothesized that, in the face of increasing pressure on environmental performance, cities with low levels of green development are more eager to seek financial and technical support. They also can improve resource allocation efficiency and release ecological dividends to enhance the endogenous momentum of green development in the city as a whole. Based on the above analysis, this study puts forward the following hypothesis.
Hypothesis 4: The lower the degree of green development of the city itself, the more significant the effect of the emissions trading system on the resilience of the city.

2.3.2 Differences in resource endowments

The effectiveness of emissions trading policies depends on the resource endowments of different urban entities. The existing studies on this issue generally include two aspects, one is the empirical study of the “resource curse” hypothesis, and the other is a consideration of the relationship between resource endowment and ecological environment (Zhou and Fang, 2019). In other words, the impact of resource endowment on urban resilience has both negative effects such as the “resource curse” and significant industrial development advantages, which have a greater effect on urban economic resilience. According to the National Sustainable Development Plan for Resource-based Cities (2013-2020) released by the State Council in 2013, all prefecture-level cities in China are divided into 114 resource-based cities and 170 non-resource-based cities, and the resource-based cities are subdivided into four categories: growing, mature, declining and regenerating. The resilience of resource-based cities is strongly influenced by their inherent industrial characteristics. As industrial advancement is constrained by resource endowments (Li and Zou, 2018), environmental regulation will have the best effect in promoting industrial advancement in the growing type. Therefore, the resilience enhancement effect of growing resource cities is most obvious after the implementation of the emissions trading system. At the same time, the heterogeneity of resource endowments leads to different intensities of environmental regulation and different effects of industrial transformation, which are mainly dependent on the existence of economic benefits (Lu et al., 2020). Specifically, for growing resource cities, on the one hand, due to their higher resource endowments, the excess profits of resource-based industries provide economic incentives for potential entrants, and the scale of emissions trading will further expand; but on the other hand, under the strict control of the emissions trading system, enterprises actively innovate clean technologies so that they have an abundant surplus of emissions quotas. Corresponding emissions trading has a broad market space, so the impact of emissions trading on the urban resilience of growing resource cities is the greatest. For regenerating resource cities, the emergence of sustainable new industries after resource depletion often results in greater urban redundancy and lower costs of pollution control, resulting in fewer emissions trading options than in growing resource cities. For mature resource cities, resource-based industries tend to be stabilized, and most enterprises can only achieve self-sufficiency in emission rights, leaving less room for emission trading. For declining resource cities, the pressure on ecological and environmental management is greater, and the demand for pollution control from enterprises is much greater than the emission trading quota, so there is little market space for emission trading. Given these considerations, the following hypothesis is put forward.
Hypothesis 5: The better the natural resource endowment conditions, the greater the policy dividend effect of emissions trading, so the enhancement effect of urban resilience is in descending order of cities characterized by growth, regeneration, maturity, and decline.

3 Research design

3.1 Model setting

In order to test the effect of the emissions trading system on urban resilience, this study used the double difference (DID) method to construct the basic model. Referring to the study of Wang and Lu (2019), the basic model designs is:
$\begin{align} & \text{ }\!\!~\!\!\text{ }scor{{e}_{it}}={{\alpha }_{0}}+{{\alpha }_{1}}treate{{d}_{i}}\times tim{{e}_{t}}+{{\alpha }_{2}}\times contro{{l}_{it}}+ \\ & \ \ \ \ \ \ \ \ \ \ \ \ \ \ yea{{r}_{t}}+cit{{y}_{i}}+{{\alpha }_{3}}pro{{n}_{j}}\times yea{{r}_{t}}+{{\varepsilon }_{it}} \\ \end{align}$
where the subscripts i, t, and j correspond to city, year, and province, respectively. The term scoredit is the dependent variable, indicating the resilience score of cityi in yeart. The grouping variable treatedi is a dummy variable for whether cityi belongs to the experimental group; and treatedi=1 if treatedi is a pilot city (prefecture-level cities in the 11 pilot provinces), otherwise treatedi=0. The term timet is the time dummy variable for the emissions trading policy, which is 1 if t is the pilot year (2007 and later), otherwise timet=0. The term controlit is the control variable group. The terms yeart and cityi are the time fixed effects and individual city fixed effects, respectively. The term εit denotes the random error term. In addition, the model introduces the term pronj×yeart to account for the unique characteristics of the city’s province in different years. In other words, this study controls the individual city fixed effects, time fixed effects, and individual province fixed effects. The estimated coefficient a1 before the term treatedi×timet is the focus, which was recorded as did. It is used for estimating the policy effects of the emissions trading system on urban resilience.
In addition to the direct effects presented in model (1), this study also verified the possible mechanisms of the impact of the emissions trading system on the level of urban resilience, including the energy saving and emission reduction effects, the level of marketization, and innovation dynamism. A moderating model was constructed by following the approach of Wang and Lu (2019) as follows.
$\begin{align} & scor{{e}_{it}}={{\beta }_{0}}+{{\beta }_{1}}treate{{d}_{i}}\times tim{{e}_{t}}\times Ad{{v}_{it}}+{{\beta }_{2}}Ad{{v}_{it}}+ \\ & \ \ \ \ \ \ \ \ \ \ \ \ {{\beta }_{3}}treate{{d}_{i}}\times tim{{e}_{t}}+yea{{r}_{t}}+cit{{y}_{i}}+pro{{n}_{j}}\times \\ & \ \ \ \ \ \ \ \ \ \ \ \ yea{{r}_{t}}+control+{{\varepsilon }_{it}} \\ \end{align}$
In model (2), Advit represents the moderating variables, specifically energy use efficiency, market-level index, and innovation dynamism level in this case. In particular, the energy use efficiency is based on Hu and Zhou (2020), and the electricity consumption per unit of GDP was chosen as a proxy indicator to reflect the effect of energy saving and emission reduction. As a preliminary exploration of market-incentive environmental regulation, the main feature of the emissions trading system is the level of marketization. For the calculation of the marketization level index, the most widely used source in academia at present is the report on China’s marketization index by province (2016). Based on its calculation rules, this study simulated the index using stata regression to obtain the 2017 and 2018 provincial index values, which were also used to obtain the marketization level index values at the prefecture level according to the most important component, i.e., the share of private enterprises in the workforce. In addition, the level of innovation dynamism was measured using the proportion of the total number of people engaged in scientific research and development. In this model, the main concern is the significance of the coefficient of the interaction term b1, and the remaining variables have the same meanings as in model (1).

3.2 Measurements of variables and descriptions

3.2.1 Measurement of urban resilience level

In this study, with reference to the studies of Li and Zhai (2017) and others, a comprehensive multi-indicator evaluation system with six sub-system levels was constructed by the entropy value assignment method, including economics, society, infrastructure, ecology, community and institutions. After distinguishing the indicators at each level as positive and negative indicators, the index of the urban resilience level was measured and used to characterize the overall development level of the cities, which was recorded as score.
(1) Economic indicators are an important dimension of urban resilience, reflecting the objective laws of economic stability and diversity as well as the overall economic capacity of the cities. Given the availability of data, this study used the per capita savings indicator for economic stability; the industrial structure rationalization index (Lv and You, 2013) and the industrial structure sophistication index (Fu, 2010) were selected to objectively quantify the characteristics of the economic diversity of each city, and the economic capacity was measured by the per capita gross regional product.
(2) Social indicators reflect the social stability and social infrastructure of cities, with social stability being characterized by the urban unemployment rate and population density, and social infrastructure being characterized by the number of health practitioners per 1000 people and the mobile phone penetration rate.
(3) Infrastructure indicators are measured by the evacuation capacity of the city, energy supply capacity, and the adequacy of medical facilities. In this study, the evacuation capacity is reflected by the road area per capita and the number of public transport vehicles per 10000 people; the energy supply capacity is characterized by water availability as well as supply capacity and energy poverty indicators; and the completeness of medical facilities is reflected by the coverage rate of medical institutions and beds.
(4) Ecological indicators measure the degree of improvement of the urban ecological environment, which portray the objective achievements of environmental management in each city and reflect the effect of their actions in response to the General Secretary’s call for “green water and green mountains are the silver mountain of gold”. In this study, the ecological indicators were measured in terms of greening coverage, pollution control pressure, and control effectiveness, with wastewater, waste gas, and smoke emissions selected to measure pollution control pressure, and control effectiveness measured by the solid waste rate, waste disposal rate and sulfur dioxide removal rate.
(5) Community indicators are also an important factor in reflecting urban resilience, and this study used the proportion of social service workers to characterize them.
(6) Institutional indicators were measured using the urban basic medical insurance rate and the unemployment insurance rate.
Table 1 shows the construction indicators, indicator weights, and indicator attributes of China’s urban resilience evaluation index system.
Table 1 Evaluation index system of urban resilience in China
Goal level Subsystem level Weight (%) Index level Weight (%) Attribute
Urban
resilience
level		 Economy 23.46 Industrial structure sophistication 0.59 +
Industrial structure rationalization 10.51 +
Per capita savings 7.37 +
Per capita gross regional product 4.99 +
Society 8.57 Urban unemployment rate 0.06 -
Population density 0.21 -
Number of health practitioners per 1000 people 2.63 +
Mobile phone penetration rate 5.66 +
Infrastructure 31.87 Road area per capita 3.39 +
Number of public transport per 10000 people 3.69 +
Water availability and supply capacity 3.64 +
Energy poverty indicators 8.08 +
Coverage rate of medical institutions 5.81 +
Coverage rate of medical beds 7.25 +
Ecology 9.98 Wastewater emissions 0.40 -
Waste gas emissions 0.64 -
Smoke emissions 0.39 -
Comprehensive utilization rate of solid waste 1.32 +
Wastewater disposal rate 1.51 +
Waste disposal rate 1.08 +
Greening coverage 0.88 +
Sulfur dioxide removal rate 3.75 +
Community 2.79 Proportion of social service workers 2.79 +
Institution 23.35 Basic medical insurance rate 11.27 +
Unemployment insurance rate 12.08 +
Based on the above urban resilience index system, the index values of 284 prefecture-level cities were measured during the period of 2003-2018, and the average value of the calculated urban resilience index (i.e., 0.152) was used as the criterion to classify the 284 cities into two major categories of high resilience cities and low resilience cities. The specific results are shown in Fig. 1. The reason for this geographical distribution is that coastal cities have natural topographical and transport advantages in terms of export trade, which is conducive to the formation of a new double-cycle development pattern and the path of high-quality economic development. Among the inland regions, the three eastern provinces have been developing vigorously, both economically and environmentally, thanks to their national policy of revitalizing the old industrial bases in the northeast. The Beijing-Tianjin-Hebei metropolitan area, with Beijing as the center, has a relatively high level of urban resilience in the cities around it, and the Beijing-Tianjin-Hebei regional development strategy has played a role in enhancing urban resilience. Meanwhile the southwest region, which relies on its resource endowment advantages, promotes the development of the local economy with ecological and environmental protection. Southwest China has become a new growth pole for China’s economy and it has a high level of urban resilience overall. On the other hand, up to two-thirds of the cities are still at a low level of resilience. The reasons for this are three-fold. In addition to the unreasonable industrial structure, the regional economy as a whole has not yet formed a synergy, which makes it difficult to give full play to the advantages of regional cooperation in urban clusters, and there is a need to continue to improve their infrastructure and basic security systems. As a result, it is necessary to enhance the resilience of cities based on the differences in their regional endowments, release the policy dividends of regional coordination and deepening development, create new power sources and growth poles for high-quality urban development, and promote the dual domestic and international cycles to enhance the resilience of cities.
Fig. 1 Distribution of urban resilience in China

3.2.2 Core explanatory variables

The core explanatory variable in this analysis is the dummy variable did and the data came from the “Guidance Opinions of the General Office of the State Council on Further Promoting the Pilot Program for the Paid Use and Trading of Emissions Rights”, which specifies the information on pilot cities, implementation time, implementation scope, etc. The did is assigned uniformly according to the time when the pilot emission trading policy was carried out. The sign of the coefficient before did and its magnitude are the keys in this analysis which reflect the change of urban resilience level before and after the pilot emission trading policy was implemented.

3.2.3 Control variables

In the quasi-natural experiment of this study, six control variables are introduced to reduce the bias of the results that can be caused by the omission of the dependent variable. Industrial structure (ind) is expressed as the proportion of total industrial output value above the limit to GDP. Usually a decrease in the proportion of industrial output value above the limit means the improvement of urban pollution control technology, which induces the decrease in the proportion of polluting enterprises. The other five variables are: environmental protection awareness (envitio), where the number of people engaged in environmental protection is divided by the total number of people at the end of the year to characterize the motivation of urban residents towards environmental protection; land use planning (areaio), where the proportion of urban construction land in the urban area represents the level of government planning; R&D and innovation capacity (fintio), where the proportion of expenditure on science and education reflects the development and innovation ability of the city’s ability to develop and innovate; the energy supply indicator (pow), which uses gas and natural gas supply to represent the level of government planning; and, finally, research and innovation vitality (scitio), which is measured by the proportion of research personnel at the end of the year.

3.3 Data sources and descriptive statistics

In this study, 284 prefecture-level cities in China from 2003 to 2018 were selected as the initial research sample, and the data were obtained from the China City Statistical Yearbook. In order to reduce the bias of the estimation due to missing data, a combination of linear filling and mean filling was used to treat the missing values in order to construct a complete data set. In addition, all continuous variables were scaled down at the 1% level to eliminate the effect of outliers. Table 2 shows the results of the descriptive statistics for the main variables. The mean value of city resilience is 0.152, with a standard deviation of 0.072, a minimum value of 0.046, and a maximum value of 0.618, indicating that the level of resilience varies considerably between cities and generally needs to be further enhanced. Regarding the control variables, there are also significant differences between cities in terms of industrial structure, R&D and innovation capacity, energy supply, innovation vitality, environmental awareness, and land use planning.
Table 2 Descriptive statistics of the main variables
Variable Symbol Computing method Sample size Mean Standard deviation
Urban resilience score Index system constructed by the entropy weight method 4544 0.152 0.072
Industrial structure ind Proportion of total industrial output value above the limit to GDP 4544 0.069 0.039
R&D and innovation capacity fintio Proportion of expenditure on science and education 4544 0.093 0.028
Energy supply pow Gas and natural gas supply 4528 0.065 0.129
Research and innovation vitality scitio Proportion of research personnel 4544 0.048 0.075
Environmental protection awareness envitio Number of people engaged in environmental protection divided by the total number of people 4544 0.111 0.089
Land-use planning areaio Proportion of urban construction land in the urban area (%) 4544 0.182 0.199

4 Results

4.1 Baseline results

This study estimated and analyzed the average treatment effects before and after the implementation of the policy through the multiplicative difference method. In order to further determine the subsequent shock effect of the policy on urban resilience, this study empirically tested the dynamic effect and changing trend of the emissions trading system on urban resilience. As seen in Table 3, model (1) controls the city individual effect, time effect, and province effect, and introduces the control variables such as industrial structure and R&D innovation ability. In the analysis of average treatment effect, the did coefficient is positive and highly significant at the 1% level after the implementation of the emissions trading system, which indicates that the implementation of the emissions trading system significantly enhanced the urban resilience of pilot cities compared with the non-pilot cities. In the dynamic effect analysis, the urban resilience of the pilot cities increased significantly at the 5% level in the first and second years following the implementation of the emissions trading system. From the third year onward, the enhancement effects of urban resilience policies in the pilot cities have been significant at the level of 1%, and the coefficients have shown a steady upward trend. This fully indicates that the emission trading system has had a continuous positive effect on the urban resilience. Overall, the regression results in Table 3 show that the emissions trading mechanism can significantly improve the resilience level of cities, and that the policy dividends are further enhanced with a sustained enhancement effect.
Table 3 Basic regression results
Variable Average treatment effect Dynamic effect
did 0.105***
(0.007)
treated×year2007 0.024**
(0.040)
treated×year2008 0.040**
(0.018)
treated×year2009 0.072***
(0.009)
treated×year2010 0.060***
(0.008)
treated×year2011 0.050***
(0.006)
treated×year2012 0.048***
(0.002)
treated×year2013 0.072***
(0.003)
treated×year2014 0.098***
(0.001)
treated×year2015 0.069***
(0.001)
treated×year2016 0.074***
(0.006)
treated×year2017 0.087***
(0.008)
treated×year2018 0.106***
(0.008)
Control variables Yes Yes
Time effects Yes Yes
Individual city effects Yes Yes
Province effects Yes Yes
Adjusted R2 0.847 0.847
N 4528 4528

Notes: ***, ** denote the 1% and 5% significance levels, respectively. All estimates control for city and year fixed effects, N is the number of samples. The did stands for the core explanatory variable. The value of a pilot city implementing an emission trading policy is 1, otherwise it is 0. The numbers in parentheses are P values.

4.2 Robustness tests

4.2.1 Common trend test

The common trend assumption is a prerequisite for the use of the double difference method, which requires that the urban resilience indices all maintain the same trend of movement in the absence of the emissions trading policy shock. The data in Fig. 2 show that this assumption was basically satisfied before the implementation of the emissions trading policy. In order to make the empirical results more rigorous, a parallel trend test was conducted following the study by Luo et al. (2015). Specifically, based on model (1) and taking the policy implementation year of 2007 as the base year, the DID regression was carried out for the explained variables of the previous years (except for 2006) and the subsequent years, respectively. The results show that none of the DID coefficients are significant in the period before the policy pilot, and their confidence intervals all include 0, which means that pilot and non-pilot cities satisfy the same trend assumption. In contrast, Fig. 2 shows the dynamic effect of the same trend test. The did coefficients changed from negative to positive after the policy implementation, and the confidence intervals do not include 0 after the second year. These results show that the implementation of the emissions trading system has induced an improvement in the level of urban resilience, with a continuous positive effect.
Fig. 2 Dynamic effect of the parallel trend hypothesis

4.2.2 Excluding energy policy effects

From the perspective of China’s environmental governance process, market incentive environmental regulation and energy policy are basically promoted simultaneously, so the impact of energy policy on the implementation of environmental regulation needs to be considered. The new energy development strategy of “Four Revolutions and One Cooperation” mainly focuses on the transformation of traditional fossil energy and the development and utilization of new energy. Among the provinces, the traditional energy transformation policies are mainly implemented in the major coal-consuming provinces such as Hebei, Shanxi, Inner Mongolia, Jiangsu, Shandong, Henan and Shaanxi, while the support policies for the development and utilization of new energy are concentrated in some western provinces (Gansu, Yunnan, Qinghai, Ningxia and Xinjiang). Therefore, in order to exclude the possible impact of energy policy on urban resilience, the samples of prefecture-level cities in the above provinces were excluded from the subsequent regression analysis.
The data in Table 4 show that the emissions trading system significantly enhances urban resilience at the level of 1% after the influence of energy policies is excluded. This implies that the emissions trading system still has a positive role in enhancing urban resilience, which further illustrates the robustness of the benchmark regression results. Compared to the full-sample estimation results, the average treatment effect analysis shows that its urban resilience level has been further enhanced, which indicates that the pilot implementation of emissions trading system in non-energy provinces is more significant. For the dynamic effect, the urban resilience enhancement effect is relatively weaker after excluding energy policies, indicating that the emissions trading system has a stronger follow-up incentive effect on the energy provinces.
Table 4 Excluding the regression results of energy policy
Variable Average treatment effect Dynamic effect
did 0.121***
(0.000)
treated×year2007 0.016***
(0.008)
treated×year2008 0.030***
(0.000)
treated×year2009 0.062***
(0.000)
treated×year2010 0.051***
(0.000)
treated×year2011 0.041***
(0.000)
treated×year2012 0.039***
(0.000)
treated×year2013 0.062***
(0.000)
treated×year2014 0.088***
(0.000)
treated×year2015 0.060***
(0.000)
treated×year2016 0.066***
(0.000)
treated×year2017 0.079***
(0.000)
treated×year2018 0.098***
(0.000)
Control variables Yes Yes
Time effects Yes Yes
Individual city effects Yes Yes
Province effects Yes Yes
Adjusted R2 0.843 0.843
N 2688 2688

Notes: *** denotes the 1% significance level. All estimates control for city and year fixed effects, and N is the number of samples. The numbers in parentheses are P values.

4.2.3 PSM-DID

The propensity score matching method (PSM) can effectively deal with the problem of “sample self-selection” in large samples. Therefore, this study used the logit model to calculate the propensity score. After comparing the matching effects of k-nearest neighbor matching, radius matching and kernel matching, the radius matching method was used to determine the weights. In addition, the covariates and their interaction terms were used as the identification features of the sample points, and the cities with more similar features were found as the estimation samples. On this basis, a series of tests were conducted. Based on the results of the PSM equilibrium test in Table 5, the absolute value of the standard deviation of all covariates after matching is below 5%. According to the T-test, all covariates after matching are non-significant, so the original hypothesis cannot be rejected and the equilibrium hypothesis is satisfied.
Table 5 PSM balance test
Covariates Mean Standard deviation (%) Error reduction (%) T-test
Experimental groups Control groups t P
gdpfin Before matching 0.082 0.103 -33.400 -10.430 0.000
After matching 0.084 0.085 -1.500 95.600 -0.550 0.585
gindtio Before matching 0.070 0.068 5.300 1.700 0.090
After matching 0.071 0.071 -0.400 92.800 -0.120 0.905
high Before matching 0.607 0.549 40.500 13.060 0.000
After matching 0.603 0.603 -0.300 99.300 -0.090 0.932
so2emil Before matching 0.746 0.831 -43.700 -14.830 0.000
After matching 0.775 0.779 -2.300 94.700 -0.710 0.476
rational Before matching 0.047 0.041 9.200 3.070 0.002
After matching 0.044 0.044 0.000 99.800 -0.010 0.996
gdpper Before matching 0.286 0.258 12.700 4.160 0.000
After matching 0.279 0.283 -1.600 87.300 -0.460 0.644
ind Before matching 0.177 0.118 30.700 10.340 0.000
After matching 0.161 0.162 -0.100 99.700 -0.030 0.978
In addition, Fig. 3 presents the results of the common support hypothesis test. Plotting the kernel density profiles of propensity scores for the experimental and treatment groups before and after matching was used to test the validity of the sample matching. The results show that the overlapping areas of the propensity scores for the experimental and control groups are further expanded after matching, and the samples approximately obeyed a normal distribution. To improve the quality of sample matching, a small number of observations that did not satisfy the test were excluded from further analyses, and the matched samples were used to estimate the policy effect of emissions trading. The regression results in Table 6 show that the policy effect is still robust under the PSM-DID approach, that is, the emissions trading system is conducive to the enhancement of urban resilience. All the above test results provide strong support for the expectations of the hypotheses of this study.
Fig. 3 PSM common support hypothesis test
Table 6 Robust regression results
Variable Average treatment effect Dynamic effect Variable Average treatment effect Dynamic effect
did 0.005***
(0.000)		 treated×year2014 0.088**
(0.018)
treated×year2007 0.023*
(0.081)		 treated×year2015 0.077**
(0.025)
treated×year2008 0.031* treated×year2016 0.086**
(0.040)
(0.072)
treated×year2009 0.040* treated×year2017 0.093**
(0.036)
(0.056)
treated×year2010 0.052** treated×year2018 0.099**
(0.034)
(0.041)
treated×year2011 0.056**
(0.022)		 Control variables Yes Yes
Time effects Yes Yes
treated×year2012 0.073**
(0.016)		 Individual city effects Yes Yes
Province effects No No
treated×year2013 0.071**
(0.020)		 Adjusted R2 0.774 0.773
N 4027 4027

Notes: ***, ** and * denote the 1%, 5% and 10% significance levels, respectively. All estimates control for city and year fixed effects, and N is the number of samples. The numbers in parentheses are P values.

4.3 Research on impact mechanisms

According to the theoretical analysis in the previous section on impact mechanisms, the effects of energy conservation and emission reduction, market-incentive mechanisms and innovation vitality may play a positive moderating role in the impact of the emissions trading system on urban resilience. To verify the effectiveness of these three mechanisms, this study selected model (2) for the empirical test, and the regression results are shown in Table 7.
Table 7 Test of the influence mechanism
Variable Energy utilization efficiency Marketization index Innovation vitality
did×Adv 0.109***
(0.005)		 0.040**
(0.014)		 0.008**
(0.036)
Adv 0.005
(0.972)		 0.138***
(0.007)		 0.794***
(0.003)
Control variables Yes Yes Yes
Time effects Yes Yes Yes
Individual city effects Yes Yes Yes
Province effects No No No
Adjusted R2 0.731 0.445 0.642
N 4528 4544 4544

Note: ***, ** denote the 1% and 5% significance levels, respectively. All estimates control for city and year fixed effects, and N is the number of samples. The numbers in parentheses are P values.

In terms of the effects of energy conservation and emission reduction, the interaction coefficient is positive and significant at the level of 1%. Therefore, the improvement in the energy conservation and emission reduction effect strengthens the positive spillover effect of the emission trading system on urban resilience, which is conducive to the positive interaction between urbanization and the ecological environment and realizes the double dividend. From the perspective of market-incentive mechanisms, its interaction coefficient is significantly positive, indicating that the improvement in the overall level of market-incentive mechanisms has injected new dynamism into urban economic development and promoted the role of the emissions trading system on urban resilience. As for the level of innovation vitality, the interaction coefficient is positive and significant at the level of 5%, which means that the level of innovation vitality of prefecture-level cities positively moderates the effect of the emissions trading system on urban resilience. Moreover, the coefficient of innovation vitality is significantly positive at the level of 1%, indicating that the implementation of the emissions trading system is conducive to stimulating the “Porter effect”. In summary, this analysis confirmed that the effects of energy conservation and emission reduction, market-incentive mechanism and innovation vitality are important mechanisms for the impact of the emissions trading system on urban resilience. Therefore, hypotheses 1 through 3 have been verified.

5 Discussion

5.1 Heterogeneous effect of the green development level of cities

According to the green development index system, this study selected the greening rate of built-up areas as the indicator of green development level, and examined the heterogeneity of green development and construction on urban resilience and environmental protection. Cities with a greening rate that is higher than the national average are defined as green cities, and cities below the average are defined as non-green cities. Under the circumstance of increasing pollution control pressure, non-green cities are more eager to seek innovative vitality, environmental improvement and technical support. In this way, the R&D and innovation ability of the whole city can be improved, and so the urban resilience will be enhanced. As shown in Table 8, the empirical results show that the improvement effect of the emission trading system on urban resilience is significant at the level of 1% for the non-green cities. Its dynamic effect indicates that the emissions trading system has a continuous positive effect on the resilience, which verifies Hypothesis 4.
Table 8 Heterogeneity analysis of the green development level
Variable Average treatment effect Dynamic effect
did 0.019***
(0.000)
treated×year2007 0.019***
(0.000)
treated×year2008 0.024***
(0.000)
treated×year2009 0.033***
(0.000)
treated×year2010 0.041***
(0.000)
treated×year2011 0.045***
(0.000)
treated×year2012 0.060***
(0.000)
treated×year2013 0.057***
(0.000)
treated×year2014 0.074***
(0.000)
treated×year2015 0.064***
(0.000)
treated×year2016 0.071***
(0.000)
treated×year2017 0.079***
(0.000)
treated×year2018 0.088***
(0.000)
Control variables Yes Yes
Time effects Yes Yes
Individual city effects Yes Yes
Province effects No No
Adjusted R2 0.771 0.757
N 1856 1856

Note: *** denotes the 1% significance level. All estimates control for city and year fixed effects, and N is the number of samples. The numbers in parentheses are P values.

5.2 Heterogeneous impact of resource endowments

Resource endowment is one of the important internal factors of the urban development strategy, and its dynamic changes have a great impact on urban resilience. This study classified resource cities in accordance with the Notice on Printing and Distributing the National Sustainable Development Plan for Resource-Based Cities (2013-2020). Double-difference fixed-effect regression was performed respectively to test the heterogeneity of resource endowments, and the results are shown in Table 9.
Table 9 Heterogeneity analysis of resource endowment
Variable Total sample Growing Mature Declining Regenerative
did 0.014***
(0.000)		 0.022*
(0.071)		 0.012***
(0.000)		 0.001
(0.836)		 0.019**
(0.028)
Control variables Yes Yes Yes Yes Yes
Time effects Yes Yes Yes Yes Yes
Individual city effects Yes Yes Yes Yes Yes
Province effects No No No No No
Adjusted R2 0.832 0.808 0.868 0.852 0.79
N 1824 224 992 368 240

Note: ***, ** and * denote the 1%, 5% and 10% significance levels, respectively. All estimates control for city and year fixed effects, and N is the number of samples. The numbers in parentheses are P values.

In general, the resilience enhancement effect of the emission trading system on resource-based cities is higher than that on non-resource-based cities. The reason may be that resource-based cities gradually transition from agglomeration diseconomy to agglomeration economy in the process of development. During that period, they are highly reliant on natural resources and mineral resources, so the total emission quota and emission demand are relatively larger, the emissions trading market is active, and the policy enhancement effect is higher than in non-resource-based cities. Additionally, among resource-based cities, the enhancement effect on growing resource cities is the most obvious, followed by the regenerative type and mature type, but it has no significant influence on the declining type for two main reasons. First, growing resource cities are in a period of rapid economic development driven by resource-based industries. While the economic benefits are significantly improving, the environmental pressure is relatively small. The market platform of emission trading not only provides the economic scheme for environmental governance, but also stimulates the ecological innovation mechanism of the cities. At the early stage of economic development, it reduces the damage to the environment, and avoids the path of development before governance from the source. Therefore, the emission trading system has the greatest effect on the resilience of growing resource-based cities. Secondly, the economic development of renewable resource-based cities is no longer dependent on traditional resource-based industries. Guided by the high-quality development of the economy and the environment, they will take an intensive and sustainable environmentally-friendly development path with emerging industries such as electronic information and tourism as the pillars. Since the rate of accumulation of traditional production factors is slower than that of the growth type, the space for the emissions trading market in such cities is slightly smaller, and the policy effect is inferior to that of the growth type. Thirdly, the contradictions between economic development and environmental issues are prominent in mature resource cities. Due to the rapid growth of resource-based enterprises, the self-sufficiency of emission quotas and the small trading market space, the emissions trading system has little impact on improving urban resilience. Finally, declining resource cities are facing crises such as the exhaustion of resource exploitation, economic downturn and environmental deterioration. The emissions trading market is likely to be at a standstill, so the emissions trading system has no significant impact on the resilience of declining resource cities. In summary, Hypothesis 5 has been verified.

6 Conclusions and policy implications

The study shows that the effects of energy saving and emission reduction, marketization level and innovation dynamics are very important factors to consider when releasing the resilience dividend of market-incentive environmental regulation instruments. On the one hand, it is absolutely necessary to constantly promote energy conservation and emission reduction and optimize the supply of “green carbon” for policymakers. Particularly in the case of large energy provinces, we should promote the improvement of the productivity of all factors in order to accelerate supply-side reforms in the energy sector. On the other hand, in order to stimulate the vitality of R&D and innovation, governments should continue to accelerate market reforms and sustainably unleash market dynamics in societies. From the perspective of the market, cities with relatively high resilience should continue to integrate industry resources, develop diversified industries and improve economic efficiency, while others need to seek regional cooperation in order to take on industrial transfers and give full play to the “automatic stabilizer” function of the regional market. At the same time, the necessary regulation is a sufficient factor for maximizing market liquidity to enhance urban resilience. From the perspective of the innovation chain, governments should focus on how to innovate the industrial structure, gather high-level talents and optimize R&D funds. Meanwhile, they need grasp the opportunity of digital development and make full use of innovation channels to enhance the resilience of cities.
This study also revealed a somewhat interesting differentiation in the aspect of the level of green development and natural resource endowment conditions of cities. Policymakers should take into account the heterogeneous needs of cities in order to make up for the shortcomings of urban dynamics. As for the aspect of the green development level, in general, the government should strengthen the performance assessment of environmental indicators and formulate more differentiated and flexible policies in terms of the environmental differences between cities. Especially for non-green cities, green innovation is the focus. Regarding the aspect of resource-based cities, the policy system needs to consider the economic, environmental and social coordination of cities. In terms of the transformation needs of resource-based cities, growing and regenerating cities should actively explore the design of effective innovation mechanisms to stimulate market activity. For mature and declining cities, more support should be provided for transformation and upgrading to enhance the city’s endogenous development momentum.

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