Does low-carbon city pilot affect the enterprise competitiveness in China? Based on a staggered difference-in-difference model

China has recently instituted the low-carbon city pilot (LCCP) policy in target areas to control its greenhouse gas emissions. Studies examining those environmental regulations have not focused on how they affect enterprise competitiveness, especially emphasizing the LCCP’s dynamic effect. Here, we use the quasi-experimental opportunities of the LCCP policy along with a staggered difference-in-difference model to evaluate and explain the influences and transmission mechanisms of the LCCP policy on enterprise competitiveness. The empirical results show that (1) the construction of low-carbon cities significantly reduces, by 3.56%, the average enterprise competitiveness. Also, capital-intensive and small firms are more susceptible to adverse effects from the LCCP policy, but those effects weaken with time. (2) The LCCP policy affects enterprise competitiveness by increasing operating costs and reducing R&D. (3) However, those adverse effects can be suppressed when a region’s degree of marketization is high and industry competition is fierce. Although our results show that the LCCP policy indeed brings more significant economic costs, those economic distortions can weaken through market-based reforms and improved market competition.


Introduction
Given the adverse impacts on human survival and development caused by global warming, carbon emission reduction has become a significant challenge for many countries worldwide. So, to reduce carbon emissions, many countries have established low-carbon cities (e.g., Seattle, London, Tokyo). China is a major developing country and one of the largest carbon emitters, so the world is very concerned about China's climate change policy. To that end, the National Development and Reform Commission (NDRC) of China instituted the low-carbon city pilot (LCCP) program in three batches of initiatives that occurred in 2010, 2012, and 2017, respectively, and they have significantly alleviated China's emission growth (Yu and Zhang 2021;Huo et al. 2022).
The LCCP policy aims to control greenhouse gas emissions by substituting traditional manufacturing-intensive industries with a newly built, low-carbon industrial system. So intuitively, the question arises as to whether the LCCP policy will impede or improve such enterprise development models. As the low-carbon cities have developed, many studies have begun focusing on the LCCP policy's macro effects. On the one hand, some scholars have discussed the environmental impacts of the LCCP policy, which show that carbon emissions and energy-intensive activities considerably declined in the pilot cities (Liu and Qin 2016;Qu and Liu 2017;Shi et al. 2018;Yu and Zhang 2021). On the other hand, many studies that focused on the economic effects of the LCCP policy at the macro level have not reached a consensus. Some scholars found that low-carbon city construction could improve the environment by optimizing energy efficiency and structure, thus stimulating industrial and technological progress and improving urbanization to achieve low-carbon economic development (Su et al. 2012;Liu and Qin 2016;Shen et al. 2018). However, Weber and Cabras (2017) argued that the newly constructed large-scale projects failed to reduce carbon emissions significantly and boost green growth. At the micro-enterprise level, competitiveness dominates environmental economics because it is fundamental for all commercial enterprise activities (Jaffe et al. 1995;Greenstone et al. 2012;Ambec et al. 2013). For the construction of low-carbon cities, the goal is to influence enterprises to achieve transformation and upgrading through policies, that is, to improve the production efficiency and competitiveness of enterprises. And the firm competitiveness directly reflects enterprises' production efficiency, which is more easily affected by environmental regulation. Therefore, firm competitiveness can better reflect the impact of the low-carbon city pilots on enterprises. Here, we both examine the effects of the LCCP policy on enterprise competitiveness and provide new evidence at the micro-enterprise level for the controversies mentioned above, thus showing a complete picture of the economic consequences of the LCCP policy. According to environmental compliance cost theory, increased environmental regulations may lead to additional compliance costs, crowd out research and development (R&D) investments, limit the progress of green technology innovation, and harm enterprise competitiveness (Jaffe et al. 1995;Tang et al. 2022). However, the Porter hypothesis holds that more stringent regulations may enhance productivity growth by causing firms to rationalize their operations (Porter and Linde 1995). As such, how the LCCP program impacts enterprise competitiveness remains to be empirically tested.
Because the LCCP 1 program in China was implemented gradually (Fig. 1), it provided an opportunity for a quasi-natural experiment and the staggered difference-in-difference (DID) model used in this paper. Based on a sample of Chinese A-share firms over the period 2008-2019, we generated evidence that the LCCP policy implementation reduces enterprise competitiveness, as measured by the enterprises' total factor productivities (TFP) calculated by the control function method of Olley and Pakes (1996). In addition, the LCCP policy's effect on enterprise competitiveness is more pronounced for capital-intensive firms, small firms, and firms with lower marketization levels and weak competition. Our findings were held throughout robustness checks.
This paper makes three key contributions to the field: (1) it contributes to research on the effect of the low-carbon pilot program by Tang et al. (2018), Cheng et al. (2019), and Song Fig. 1 Low-carbon city pilot (LCCP) policy implementation in China 1 "Pilot policy," sometimes simplified to "pilot," is a unique and innovative practice used in China for policy testing and includes various forms of pilot projects and pilot areas. Such projects focus on the time dimension and are the most typical and common types of policy pilots in China's policy process. However, previous researchers who assumed that the effect and intensity of policies do not change over time may have drawn incorrect conclusions because of that assumption. Second, some cities in the first LCCP batch did not reach an ideal environmental performance goal because, facing the pressure of maintaining the local economy's growth rate, local governments applied lax low-carbon city development planning. However, the entities of the second and third batches, bolstered by their local governments' positive initiatives, gradually achieved acceptable carbon emission goals. Therefore, we consider the time-varying effect of the LCCP by using the staggered DID model to examine the effect of the LCCP on enterprise competitiveness. et al. (2020) from the perspective of enterprise competitiveness. Since enterprise competitiveness is fundamental for all commercial enterprise activities, our findings helped us extrapolate the program's micro-effect; (2) this paper provides more evidence for the debate on whether the Porter hypothesis works or the compliance cost theory works in the relationship between environmental regulation and enterprise competitiveness, our results support the compliance cost theory, but those effects weaken with time; (3) unlike previous papers based on a single batch of LCCP implementation or other one-time environmental regulations, we first applied the staggered DID model based on three LCCP sets and examined the dynamic effect of environmental regulations, which avoids the contemporaneous trend of other confounding effects and produces a more convincing causality inference.
The remainder of this paper is organized as follows. Section "Institutional background and hypothesis development" introduces the institutional background and develops the hypotheses. Section "Research design" introduces the research data and methodology. Section "Empirical results and analyses" presents the empirical and robustness test results. Section "Heterogeneity analyses" offers the heterogeneity analysis and, section "Conclusions and policy implications" concludes the paper.

Institutional background and hypothesis development
The low-carbon city pilot policy Establishing low-carbon cities has gradually become a worldwide method for tackling global warming. As the top emitter of greenhouse gases, China faces severe domestic and foreign pressure to reduce carbon emissions. Given this context, China developed a low-carbon city program in its 12th Five-Year Plan. 2 In 2010, the NDRC issued a "Notice on Carrying out Low-carbon Province and Low-carbon City Pilot Work" that designated Guangdong, Liaoning, Hubei, Shaanxi, and Yunnan, as well as Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang, and Baoding as the first national low-carbon pilot provinces and cities, respectively. In 2012, the NDRC issued a "Notice on the Implementation of the Second batch of National Low-carbon Province and Low-carbon City Pilot Work" and determined that the second batch of national lowcarbon province and city pilot work would be instituted in 29 cities, including Beijing, Shanghai, Hainan, and Shijiazhuang. Most recently, in 2017, the NDRC issued a "Notice on the Third Batch of National Low-carbon City Pilot Work" and instituted that batch of LCCP work in 45 cities (districts and counties) ( Fig. 1). Low-carbon city construction is a significant strategy for China's economic and social development, as well as a critical opportunity to accelerate the transformation of the economic development model and of financial restructuring.
The LCCP aims to implement low-carbon economies in cities, including low-carbon production and consumption, to both establish a resource-saving and environmentally friendly society and build a benign and sustainable energy ecosystem. To achieve the aim, low-carbon city will adjust the urban industrial structure as a primary way to reduce carbon emissions and thus achieve low-carbon industrial development. China needs to achieve low-carbon upgrades through the technological transformation of traditional industries while also promoting industrial structure upgrades and actively developing low-carbon industries. Thus, on the one hand, business projects and industries must adopt government mandated interventions and restrictions. On the other hand, the government, through fiscal subsidies, tax incentives, and other policies, also encourages enterprises to increase investment in environmental protection. So, does the LCCP policy affect enterprise competitiveness and, if so, how is it affected?

Research hypotheses
The relationship between environmental regulation and firm competitiveness is still controversial. There are two classical theories: compliance cost theory which argues that environmental regulation will bring more costs to enterprises, which is equivalent to imposing new constraints on the company's production decisions (Jaffe et al. 1995;Palmer et al. 1995), and inhibit the original technological innovation of enterprises (Schmalensee 1993;Li and Wu 2017), resulting that enterprise TFP will decline (Tang et al. 2020). While the Porter hypothesis argues the opposite relation that appropriate environmental regulations can lead to "innovation offsets," which not only improve environmental performance but also increase enterprises' efficiency (Porter and Linde 1995). The low-carbon city pilot policy is a new type of environmental regulation instrument that integrates mandatory and market-based environmental regulation instruments. So, does the LCCP policy affect enterprise competitiveness and, if so, how is it affected?
The low-carbon city pilot policy requires that enterprises adopt development processes with lower carbon footprints than what had been previously required. Enterprises in the pilot areas face stricter environmental regulations, such as compulsory intervention and restraint on enterprises and harsher penalties when they release pollutants into the environment. To achieve the dual goals of low carbon and green development in the beginning, an enterprise should adjust its input factor structure, such as by investing in pollution-reducing projects or increasing capital investment (e.g., by installing energy-saving equipment to reduce pollution). Therefore, enterprises in low-carbon cities probably must passively adjust their input factor structure so that they inevitably invest greatly in environmental projects instead of in their main operation activities, a process that greatly increases their short-term production, operation, and management costs (Berman and Bui 2001;Ambec et al. 2013;Greenstone 2002). Ultimately, this hurts enterprise competitiveness, at least in the beginning.
However, when enterprises accept that such environmental regulation is a long-term policy, they might switch from investing in pollution remediation to investing in R&D for pre-emission reduction, thus naturally promoting more efficient industrial upgrades (Ambec and Lanoie 2008;Jaffe et al. 2002). According to the Porter hypothesis, appropriate environmental regulations lead to "innovation offsets," which not only improve environmental performance but also increase the total factor productivity (TFP) of enterprises (Porter and Linde 1995). So, in a long time, when faced with increased environmental compliance costs (carbon emission costs, production costs, and their expectations of future costs, etc.), companies will exert efforts to maximize profits by improving their technological innovation to increase production efficiency (Bu et al. 2020), leading to the improvement of enterprise competitiveness. So under the influence of two theories, we argue that the adverse impacts of the LCCP policy on enterprise competitiveness should gradually weaken with time. Therefore, we propose two competing hypotheses: Hypothesis 1: The LCCP policy reduces enterprise competitiveness, but those adverse effects weaken with time.

Data and sample
We began constructing our research sample by finding all Chinese firms that were A-share listed from 2008 to 2019. Our sample consisted of cross-sectional data compiled in a firmyear structure. We then excluded firm-year observations that had missing information for control variables and companies with only one firm-year observation. Finally, we winsorized the continuous variables at the 1 and 99% levels to mitigate the effects of outliers. Our final sample included 21,249 firm-year observations.
We used the China Stock Market and Accounting Research database, the leading provider of corporate financial data in China, to obtain data on our target companies. Urban characteristics and local environmental indicator data were obtained from the National Bureau of Statistics of the People's Republic of China website. In addition, our province-level marketization index data was gleaned from Wang et al. (2018).

Empirical model
Here, we regard the LCCP policy as a quasi-natural experiment. Since the NDRC established three batches of low-carbon pilot projects in six provinces and eight cities, 28 cities, and 46 cities in 2010, 2012, and 2017, respectively, we use the staggered DID model. 3 The following fixed effect model of time and firm tests the LCCP impact on enterprise competitiveness. where TFP_OP stands for enterprise competitiveness for company i in year t ' D is a dummy variable (the core explanatory variable) that equals one in the years after company i became regulated under the LCCP and equals zero otherwise; the coefficient 1 represents the LCCP policy impact on enterprise competitiveness, Control is a set of control variables (Appendix A); t is year fixed effects; i is firm fixed effects; and it is a random error term.

Enterprise competitiveness
Since TFP reflects the output of an enterprise's activities, we considered the TFPs of the companies in our study to be the explained variable that measures enterprise competitiveness. We provide the calculation process below based on the uniform semi-parametric estimation method proposed by Olley and Pakes (1996).
Based on the a well-recognized Cobby-Douglas production function, To alleviate the simultaneity bias and selectivity and attrition bias of the Cobby-Douglas production function, Olley and Pakes (1996) used current enterprise investment as a proxy variable for unobservable productivity shocks and so constructed the following investment function and its inverse function.
Thus, the initial production function (2) changes into In Eq. (2), y denotes the output of the enterprise i in year t; k represents the current capital deposit of enterprise i of year t; l represents the current labor of enterprise i of year t; ω denotes the unobservable productivity of the enterprise i in year t, and e represents the residual term in the regression model. Equation (3) establishes the relationship between the current capital deposit, k, and the investment amount, i, of an enterprise, and Eq. (4) constructs an optimal investment function where it is a factor that cannot be observed by enterprises and that affects the selection of current input, except for the input of labor and capital. Finally, Eq. (4) substituted into the Cobby-Douglas production function estimation equation creates Eq. (5) and estimates the TFP value. (1)

Other variables
Controls represent variables that may influence enterprise competitiveness and include market value (Size), established age (Age), ownership type (SOE), profitability (ROA), book-tomarket ratio (BM), leverage (AL), industry competition (HHI), industry pollution (Pollution), and the province's urban green coverage rate (Green). We also controlled the fixed effects, year, and firm. All variables are defined in Appendix A.

Descriptive statistics
The main variables' descriptive statistics (Table 1) suggest that, on average, compared with the control group, the treatment group's TFP_OP is 0.246% higher. That preliminarily it indicates that low-carbon pilots have a positive effect on enterprise competitiveness.

Impact of LCCP policies on enterprise competitiveness
After controlling for only year and firm effects, we found that China's LCCP policy has significantly reduced enterprise competitiveness (Table 2). Even after serial testing with each control variable added to the model, the results generally remain unchanged and usually significant (Table 2). This indicates that compared to companies in non-LCCP areas, those under the LCCP policy initially experienced significantly reduced enterprise competitiveness (− 3.56%), thus showing that environmental regulations negatively influenced on enterprise competitiveness. However, 5 years later, the LCCP companies' enterprise competitiveness had reversed to a positive position, thus indicating that the earlier adverse effects weakened with time and preliminarily verified Hypothesis 1.

Parallel trend analysis
Before adopting the staggered DID method for analysis, the premise condition of the common trend must be satisfied. 4 Without policy interference, the development trend results for the experimental and control groups should be the same. So, upon investigation, two key points arose: changes in enterprise competitiveness did not precede LCCP implementation and the impact of LCCP policy on enterprise competitiveness presented a long-term reversal trend (Fig. 2). Also, immediately after the LCCP was instituted, enterprise competitiveness fell leaving Year0 negative and significant at the 1% level. The impact of the LCCP on inequality grew for about 6 years after the LCCP began, and then the effect leveled off. In sum, changes in enterprise competitiveness did not precede LCCP implementation, and LCCP policy has a negative effect on enterprise competitiveness.

Mechanism analysis
In this part, we will test the mechanism of the LCCP on the firm competitiveness. The main test shows that LCCP policy reduces enterprise competitiveness, supporting the compliance cost theory. Thus, we will argue two mechanisms as follows. First, we argue that the LCCP policy reduces enterprise competitiveness by causing increased operating costs. Compliance cost theory argues that environmental regulation increases enterprise costs because it is like imposing new constraints on a company's production decisions (Jaffe et al. 1995;Palmer et al. 1995). The LCCP project urges enterprises to reduce the use of coal and to seek new clean energy sources actively. So, while trying to develop new energy sources, enterprises are burdened with extra costs for new expenses such as emission detection and evaluation and low-carbon emission equipment. In addition, enterprises must also pay to train employees in Fig. 2 The dynamic impact of LCCP on coefficient of enterprise competitiveness energy conservation and emission reduction knowledge and skills, thus increasing salary expenditures for green production R&D personnel, carbon emission inspectors, and other such jobs. Companies may also have to pay fines if they fail to properly implement environmental policies. So, by most likely increasing enterprise operating costs, the LCCP policy would reduce enterprise competitiveness. Then, we propose the second mechanism that the LCCP policy reduces enterprise competitiveness by impeding enterprise innovation. Combined with the crowding-out effect and compliance cost theories, environmental regulation can inhibit original technological innovation (Schmalensee 1993; Li and Wu 2017). When enterprises face the realities of external environmental regulations, they must transfer resources initially earmarked for technological innovation to pollution control to avoid punitive damages from environmental pollution infractions. In taking priority over R&D, those actions squeeze out resource inputs to technological innovation, thus reducing enterprise innovation. Enterprises in the pilot areas must control their total greenhouse gas emissions and formulate plans for allocating greenhouse gas emission targets. So for each affected company to achieve their emission reduction target, they may immediately allocate more resources to environmental governance by sacrificing short-term technological innovation. That means that environmental inputs and product innovation may be mutually exclusive, and thus the LCCP policy will hurt enterprise innovation in the short term.
We used the mediating effect model to examine how the LCCP policy affects enterprise competitiveness through increased operating costs and impeded innovation. First, we used the mechanism indicator as the explained variable to test the effect of D, and then we added the mechanism indicator into the basic model. We found that China's LCCP policy had significantly increased enterprise operating costs and those increased costs had reduced enterprise competitiveness  (Table 3). The results were similar when innovation was examined. They indicated that China's LCCP policy reduced enterprise competitiveness by reducing R&D (Table 3).

Robustness tests
We performed four robustness checks to examine the validity of our results. They included using alternative enterprise competitiveness measures, a placebo test, an IV test, testing with other policies excluded, and controlling the effect across times and regions.

Alternative measures of enterprise competitiveness
Besides the OP method, we used three other ways to calculate TFP and thus test the robustness of our previous conclusions. The technique used by Levinsohn and Petrin (2003, TFP_LP) allows for flexibility in selecting proxy variables according to the available data's characteristics because it substitutes the input for intermediate input. Therefore, to begin a test of robustness, we constructed a TFP_LP model to measure enterprise competitiveness. We also constructed two other TFP indicators to measure enterprise competitiveness (TFP_OLS, TFP_FE 5 ) and boost our robustness testing (Table 4). The explanatory variable D was significantly negatively correlated with all three alternative enterprise competitiveness measures (TFP_OLS, TFP_FE, TFP_LP), thus supporting our Hypotheses 1.

Placebo test
Following Cai et al. (2016), we set an artificial implementation year of the LCCP policy (2 years before) for the experimental group. A non-significant coefficient would indicate that the improvement in enterprise competitiveness was caused by the LCCP policy. Otherwise, our conclusions would not be robust. As predicted, the coefficient estimation of D, the core explanatory variable, was not significant, so the enterprise competitiveness declines of listed companies  (Table 5). Based on the results of this placebo test, we reasonably showed that our previous conclusions are robust and verified Hypothesis 1.

Instrumental variable analysis
Considering that pilot areas may have been selected non-randomly, we chose IV estimation to solve that endogenous problem, using regional air quality (AirQ) as an IV (see Appendix A), according to Xavier et al. (2012). In theory, air quality was determined by meteorological factors such as wind speed and the height of the atmospheric mixed layer. So, the utterly exogenous weather is used as a tool variable to remove the endogenous between the company's competitiveness and selecting the pilot cities by the policy maker. The weather in the previous year could be an important factor for the policy maker in choosing the pilot city. However, it has little impact on the operational activity of the enterprise in the current year. Therefore, the IV variable is not correlated with the error term and satisfies the exogenous assumption. So, that variable satisfied the correlation and exogenous assumptions of influential instrumental variables.
The results of an F-test eliminated the potential problem of "weak instrumental variables" (Table 6). Then we found that the probability of a region being selected as an LCCP area was positively correlated with air quality. Subsequent regression results were similar to those of the previous benchmark regression results (Table 2), further verifying the negative impact of the LCCP policy on the enterprise competitiveness of Chinese A-share companies.

Exclusion of other policies
Estimating the impact of the LCCP policy on enterprise competitiveness may face interference from other policies, which may cause inaccurate benchmark regression results. For instance, after coming into office in 2013, the new government promulgated a series of policies and regulations to improve ecological efficiency. Therefore, to ensure the robustness of our empirical results, we added two dummies to the benchmark regression results. New Energy represents the new energy city policy 6 implemented in 2014, and Belt&Road represents the belt and road initiative 7 implemented in 2017 (Appendix A). After adding those environmental policies, the regression coefficients for our variable of interest (D) remained significantly negative (Table 7), thus indicating that our results remain reliable after considering the impacts of other policies.

The effect across times and regions
In addition, to ensure the robustness of our empirical results, we control the effect of province and province × year fixed effects and cities and city × year fixed effects to guarantee the policy effect remains stable across times and cities. The results are listed in Table 8. Our results are robust after considering the effect across times and cities (province). After adding those time and region fixed effects, the regression coefficients for our variable of interest (D) remained significantly negative (Table 8), thus indicating that our results remain reliable after considering the effect across times and regions. A new energy city is supposed to make full use of the abundant local renewable energy sources (e.g., solar, wind, geothermal, biomass) so that energy consumption consists of a higher proportion or a larger utilization scale of renewable energy. To date, the policy covers 81 cities. Therefore, New Energy is 1 when a city falls within the scope of the new energy city policy, otherwise it is 0. 7 An important concept of China's belt and road initiative is cooperation at the aim of ecological civilization construction among the 18 included provinces. Therefore, Belt&Road is 1 if a province falls within the scope of the belt & road initiative, otherwise it is 0.

Firm characteristics
A significant feature of an enterprise is its size. A large enterprise has more resources than a small one and can effectively allocate them to deal with adverse impacts of mandated policies so that their original operation activity is not significantly affected. However, small enterprises have no choice but to divert their production resources to comply with environmental governance, so they are more affected by such policies. So, we conjecture that the relationship between the LCCP policy and enterprise competitiveness is more vital for small firms than for large ones. To examine our conjectures, we first divided the total sample into two subsamples according to the median of firm size: either larger or smaller size. While the large firm subsample displayed no significant relationship between the LCCP policy and enterprise competitiveness, the small firm subsample showed a significantly negative correlation between the LCCP policy (D) and enterprise   (1) and (2) in Table 9), thus supporting our assumption. Capital-intensive enterprises are more likely to become the main object of environmental regulations because their higher pollution emission intensity renders them more susceptible to environmental regulations (Wu et al. 2019). Therefore, we conjecture that the relationship between the LCCP policy and enterprise competitiveness is stronger for firms with higher capitalization than for firms with lower capitalization. Consequently, we divided the sample into two subsamples according to the median of capital intensity (Capint, see Appendix A): either higher or lower capitalization. The higher capitalization subsample showed no significant relationship between the LCCP policy and enterprise competitiveness. However, for the lower capitalization subsample, the LCCP policy (D) was significantly negatively correlated with enterprise competitiveness (TFP_OP) (Columns (3) and (4) in Table 9), thus supporting our assumption.

Industry and region characteristics
Most enterprises exist in fiercely competitive environments, and innovation is an important way for them to gain advantages against their rivals (Ireland and Webb 2007). So when environmental regulations become more stringent, enterprises need to balance their relationships between environmental governance and technological innovation. If such enterprises choose environmental management at the expense of technological innovation, they can lose their competitive edge; therefore, enterprises facing serious competition will have a great awareness of the importance of technological innovation, and rationally allocate enterprise resources. Thus, those companies will improve enterprise competitiveness and be less affected by the LCCP policy. To investigate that idea, we divided the total sample into two subsamples, according to the median of the Herfindahl-Hirschman Index (HHI, see Appendix A): either fierce or weak competition. We found no significant relationship between LCCP policy and enterprise competitiveness in the fierce competition subsample; however, in the weak competition industry subsample, the LCCP policy (D) was significantly negatively correlated with enterprise competitiveness (TFP_OP) (Columns (1) and (2) in Table 10), thus supporting our assumption. Forty years of rapid development and growth have transpired since China's reform and opening-up in 1978 (Tang et al. 2021), and the external governance environment has dramatically improved during that time. However, China's vast territory and unbalanced regional development have contributed to great differences in the external governance environment of each region (Zhang 2021). In lower marketization regions, enterprises have a high degree of information asymmetry with outside investors (Liu et al. 2021), leading to severe financial restrictions. In those cases, when the LCCP policy causes increased costs, many enterprises cannot obtain sufficient funding from the external market and that leads to reduced R&D investment. So, we conjecture that the adverse effects of the LCCP policy on enterprise competitiveness are more substantial in lower marketization regions. To investigate that idea, we used marketization index (MI) data from Wang et al. (2018) and divided that sample into two subsamples according to the MI median: higher or lower marketization. We subsequently found that the LCCP policy had no significant impact on enterprise competitiveness in the higher marketization group, but the coefficients of D on enterprise competitiveness were significantly negative at the 1% level in the lower marketization group (Columns (3) and (4) in Table 10), thus supporting our assumption.

Conclusions and policy implications
Based on firm-level data in China from 2008 to 2019, we used staggered DID, the parallel trend test, the IV method, the placebo test, and other methods to empirically test the impact of three batches of LCCP policy implementation on enterprise competitiveness. Our resulting conclusions are as follows: (1) The construction of low-carbon cities significantly reduced enterprise competitiveness based on TFP, but those adverse effects weakened with time.
(2) Impact mechanism analysis showed that the LCCP policy increased company operating costs and reduced R&D. (3) The heterogeneity analysis of firms shows that capital-intensive and smaller firms are more susceptible to the adverse effects of environmental regulation. And at the same time, higher marketization and industry competition can suppress the adverse effects of environmental regulation.
The construction of low-carbon cities is a common way to reduce urban carbon emissions. It has positive economic effects for enterprises in developed cities such as London and Paris. However, for developing countries that sometimes lack modern and effective laws and systems, environmental policies are often difficult to implement, and the anticipated result is unattainable. The background of China, a leader among developing countries, has been ignored by previous low-carbon city development research. So given that background and having considered the time-varying dimensions and dynamics of the LCCP effect on Chinese firms, we propose the following policy improvements: (1) Low-carbon city construction should continue exploring ways to advance the industrial structure. After falling under the LCCP policy, firms experienced significantly reduced enterprise competitiveness caused mainly by increased operating costs and reduced R&D. Therefore, the governments of LCCP areas should compensate enterprises for their increased operating costs, at least in the Table 10 Heterogeneity analysis of the impact of competition and marketization on the relationship between LCCP policy and enterprise competitiveness This table presents the results of the impact of region marketization and industry competition on the relationship between LCCP policy and enterprise competitiveness. The t-values (in parentheses) are based on robust standard errors clustered by firm. All variables are defined in Appendix A * , **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively early stage of policy implementation. We also suggest that they build technology innovation platforms, create innovation environments, and enhance the willingness and ability to undertake green innovation.
(2) Improvements in the implementation of the LCCP policy should vary based on a firm's market capitalization and size. Specifically, capital-intensive firms and small firms should receive more government assistance to help them improve resource allocation efficiency and strengthen their technological innovation.
(3) The government should further improve marketization to ensure effective policy implementation. Fierce competition can accelerate the transformation and upgrade of enterprises and raise competitiveness. In sum, the best route to success would be to promote pollution controls and pre-polluted upgrades synchronously, to design special low-carbon city modes tailored for each district, and thus improve both the competitive environment for enterprises and the capital market.
Acknowledgements Wethank Ilhan Ozturk (the editor) and the anonymous referees for their helpful comments. And we express our gratitude to SCINET (www. scinet. com. cn) for the expert linguistic services provided.
Author contribution We confirm that the manuscript has been read and approved by all the named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We state and confirm the contributions of each author. Mengran Duan (first author): conceptualization, methodology, investigation, formal analysis, writing -original draft; Ruiyao Xu (corresponding author): empirical analysis, conceptualization, supervision, writing -review and editing. We understand that the corresponding author is the sole contact for the editorial process. She is responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs. All persons who have made substantial contributions to the work reported in the manuscript, including those who provided editing and writing assistance but who are not authors, are named in the Acknowledgements section in the title page.
Funding This work was supported by the Research Foundation for Youth Scholars of Beijing Technology and Business University, China (Grant No. PXM2020_014213_000017).

Data availability
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request. The datasets generated during and/or analyses during the current study are available in the CSMAR.

Declarations
Ethical approval and consent to participate Not applicable.

Competing interests
The authors declare no competing interests.