Low-carbon governance, scal decentralization and sulfur dioxide emissions: Evidence from a quasi-experiment with Chinese heavy pollution enterprises

25 This paper investigates the effects of enterprise environmental governance under low-carbon pilot policies in China 26 with a difference in differences (DID) design. In examining the development of these policies, we focus on 27 exploring their effects on sulfur dioxide emissions of heavily polluting enterprises based on prefectural city- and 28 firm-level data from 2003-2014. Overall, the policies significantly increased enterprise SO 2 emissions, and the 29 underlying reason being that investments in CO 2 control crowded out investment in SO 2 control in enterprises in 30 low-carbon pilot regions. We also find that the implementation of low-carbon pilot policies resulted in greater SO 2 31 emissions from state-owned enterprises and enterprises in western regions than from non-state-owned enterprises 32 and those in eastern regions. It is further found that fiscal decentralization and the associated mediating effect of 33 market segmentation promote enterprises' CO 2 control and inhibit their SO 2 control. This study helps us re- 34 examine the overall environmental effects of low-carbon policies and has implications for the revision and 35 improvement of environmental governance policies in developing countries.

implemented in London on regional air quality. Wolff (2014) assessed the impact of low-emission area policies 51 implemented in Europe on regional air quality by using a difference-in-differences (DID) design to determine the 52 treatment effects across regions and over time. Gehrsitz (2017) also used DID to investigate the effect of low-53 emission zone policies implemented in Germany on air quality and infant mortality. All of the above studies found 54 that low-emission policies significantly improved air quality in the regions where the policies were implemented. 55 To improve air quality and control environmental pollution, the Chinese government has also developed 56 and implemented a series of environmental governance measures, with the Low-Carbon Pilot Policy (LCPC) being 57 one of the most important institutional arrangements. In July 2010, the Chinese government issued a notice on the 58 first round of low-carbon provincial and municipal pilot programs and areas, including Guangdong, Guiyang and 13 59 other provinces and cities. In November 2012, the "Notice on the Second Batch of Low-Carbon Provincial and 60 Municipal Pilots" was issued, covering 29 provinces and cities such as Hainan and Zhenjiang. Numerous studies 61 are some institutional weaknesses, especially the lack of a clear definition of low-carbon pilot areas, an effective 138 evaluation system and comprehensive development goals and the implementation of multiple parallel programs that 139 confuse the process (Khanna et al. 2014). In turn, local governments lack awareness over the progress of the low-140 carbon economic transition and clarity surrounding the concepts of energy conservation and a circular, low-carbon, 141 sustainable economy. This irrational design and consequent implementation problems lead to distortions in resource 142 allocation and efficiency losses, which can easily lead to a green paradox (Sinn 2008). 143 At the pilot region level, the LCPC imposes clear CO 2 emission reduction requirements (Feng et  regions use a combination of three regulatory tools, namely, mandates, market tools and voluntary initiatives, to 146 pursue policies (Wang et al. 2015), the specific implementation process uses mainly mandates (Xu and Cui 2020): 147 for example, shutdowns of enterprises violating CO 2 emission standards, setting of mandatory CO 2 emission 148 intensity targets per unit of GDP, and delegation of CO 2 emission control to lower levels of government and 149 enterprises (Song et al. 2021). In addition, government officials in the pilot regions generally regard the central 150 government's assessment targets for CO 2 emission reduction as their top priority because compliance affects their 151 personal careers. In addition, they pay no attention to emissions other than those targeted for assessment unless they 152 become components of the higher-level assessment (Qi 2013;NDRC 2014). As a result, the intensity of CO 2 153 emission control may be greater than that of SO 2 emission control in low-carbon pilot regions in China. 154 For enterprises, as rational economic agents, the optimal choice in complying with the LCPC is to increase 155 their investment in CO 2 treatment. This inevitably requires significant financial support, forcing enterprises to 156 redirect their environmental funds to reducing CO 2 emissions. However, many enterprises in heavily polluting 157 industries have limited environmental protection expenditures because of financial and technological constraints 158 , and enterprises may reconfigure these expenditures in the presence of regulation. This may make it 159 difficult to increase capital investment in the treatment of SO 2 in line with increases in enterprise production. Thus, 160 the implementation of the LCPC and increase in investment in CO 2 control is likely to be accompanied by a 161 crowding-out effect on investment in SO 2 control, with SO 2 emissions among heavy polluters in pilot areas 162 correspondingly increasing. 163 164

Research hypotheses 165
Drawing on the framework of Berman and Bui (2001) and , we construct a production function that 166 includes "quasi-fixed" input factors, calculate firms' pollution emissions, and then introduce environmental 167 regulations into the pollutant function. In particular, according to Brown and Christensen (1980), "quasi-fixed" input 168 factors can be determined by exogenous constraints. As the main tool for the LCPC, the command-and-control 169 policy requires firms to meet emission standards by a deadline, and firms must invest more in CO 2 emission control 170 in the short term or have their operations suspended or even shut down. Therefore, we consider the pollutant 171 treatment inputs for LCPC compliance to be "quasi-fixed" input elements. 172 Assume that a cost-minimizing heavy polluter operates in a perfectly competitive market. The capital 173 quantity k is the sum of the "quasi-fixed" input z(z= + ) and the fixed input u. The production function has the 174 following form: 175 where q is the output, l is the labor input, and , are the CO 2 treatment input the SO 2 treatment input of the 177 LCPC, which we consider "quasi-fixed" input factors. We use a linear equation to approximate: 178 (2) 179 Referring to the Levinson (2009) approach, total emissions of pollutants E are assumed to be: 180 = * (3) 181 In the above equation, v is the pollutant pollution emission intensity, and q is the output. Equation (2) 182 brought into equation (3) gives: 183 Referring to Li and Peng (2013), we can simplify the effect of environmental regulation (R) on pollution 185 emissions as: 186 The impact of environmental regulation (R) on pollution emissions is achieved through the following 188 mechanisms: 189 The input factor market is assumed to be perfectly competitive, so any change in environmental regulation 191 will not affect factors l and u. In addition, the pollutant emission intensity is determined by the firms' emission 192 reduction technology and emission reduction equipment, which are not affected by the environmental regulation in 193 the short run. Therefore, the first and second terms in equation (6) are dropped, leaving the third and fourth terms. 194 These terms reflect the impact of the LCPC on the "quasi-fixed" CO 2 and SO 2 treatment inputs, respectively. 195 Because the LCPC regulates CO 2 more strongly than SO 2 emissions, i.e., > , > . Becase z= 196 + , in the case of z remaining unchanged, enterprises can only control the "quasi-fixed" SO 2 treatment inputs 197 ( , i.e., SO 2 governance input) by crowding out "quasi-fixed"CO 2 treatment inputs. 198 Therefore, with z(z= + ) held constant, > 0 and < 0. Hence, >0 and <0. 199 In addition, it has been shown (Copeland and Taylor 2013) that / >0, so that we can derive > 0 200 and < 0. It is clear that the LCPC has a crowding-out effect on the "quasi-fixed" SO 2 control inputs. If the 201 "quasi-fixed" SO 2 control input is reduced, S 2 emissions increase. Based on this, this paper proposes the 202 following. 203 Hypothesis 1: Low-carbon pilot policies aggravate SO 2 emissions by heavy polluters. 204 Hypothesis 2: Low-carbon pilot policies increase CO 2 inputs and inhibit S 2 inputs among heavy 205 polluters. 206 In addition, in China's low-carbon pilot regions, financial support is an important institutional arrangement 207 for CO 2 governance. The low-carbon planning programs of the pilot regions have proposed various low-carbon 208 financial policies to reduce CO 2 emissions, including special funds for low-carbon development; industry subsidies, 209 preferential loans with reduced interest rates, and specific loan funding arrangements for CO 2 reduction; and low-210 carbon tax exemptions. These financial policies can increase investment in low-carbon projects and direct more 211 capital to low-carbon industries and production processes by allocating capital among different types of industries, 212 thus alleviating the financing constraints that enterprises may face and helping them reduce their CO 2 emissions 213 (Wang et al. 2019). This low-carbon finance policy focuses on management of CO 2 emissions and requires 214 enterprises to meet certain treatment input requirements for CO 2 reduction. However, the LCPC does not set out a 215 financial support policy for reducing SO 2 emissions; thus, enterprises in high-pollution industries are more willing 216 to invest in governance to meet CO 2 emission standards and to complete the tasks assigned by local governments 217 but less willing to invest in governance of SO 2 and other pollutants, which may exacerbate SO 2 emissions. 218 Accordingly, this paper proposes the following. 219 Hypothesis 3: Low-carbon pilot policies related to financing lead heavy polluters to increase their CO 2 220 treatment inputs and inhibit SO 2 treatment inputs through a crowding-out effect on SO 2 reduction inputs. 221 222 3. Data and empirical strategy 223 224

Data sources 225
To comprehensively examine the impact of the LCPC on the SO 2 emissions of heavily polluting enterprises and its 226 influence mechanism, this paper integrates multiple sets of statistical data and finally integrates them to construct a 227 comprehensive database including Chinese industrial enterprise data, enterprise pollution data, and municipal-level 228 statistics. The details are as follows. 229 First, we use data on Chinese industrial enterprises. The data come from the National Bureau of Statistics, 230 covering all industrial enterprises above a certain size. This database contains basic information such as the 231 enterprise name, legal person code, enterprise address and many financial indicators such as total assets and sales. 232 This database, which offers the advantages of a large sample size and rich information, has been widely used in 233 recent studies. Referring to Brandt et al. (2012Brandt et al. ( , 2017 and others, the following processing was performed on the 234 database of industrial enterprises before matching: (1) enterprises with duplicate legal person codes were eliminated; 235 (2) enterprises whose data do not comply with general accounting standards (e.g., had current assets exceeding total 236 assets, net fixed assets greater than total assets, or a missing number of employees) were eliminated;

Model specification 275
The question explored in this paper is the effect of the LCPC on SO 2 emissions from heavy polluters. To address the 276 endogeneity problems commonly faced in the literature, this paper constructs a multiperiod double-difference model 277 using the LCPC as a quasinatural experiment, divides the study population into a treatment group (areas where the 278 policy has been implemented) and a control group (areas where the policy has not been implemented), and removes 279 the time trend. The net effect of the policy implementation is identified by differentiating the time trend before and 280 after policy implementation and the difference between the treatment and control groups to isolate the policy effect 281 from the influence of time-varying and unobservable factors. This method has been widely used in existing policy 282 studies (Song et al. 2019). In this paper, the provinces and cities included in the scope of the first two rounds of low-283 carbon pilot projects are used as the treatment group, and the remaining provinces and cities are used as the control 284 group to quantitatively assess the effect of LCPC implementation on SO 2 emissions from heavily polluting 285 enterprises. The specific model settings are as follows: 286 logarithm of total firm capital at the end of the year is used to measure the firm size. (ii) Firm age (age). The age of a 317 firm usually represents its maturity, and studies have shown that more mature firms tend to have stronger operational 318 capabilities (Huang et al. 2021). In this paper, the number of years that a firm has been in business since its inception 319 is used to measure firm age. (iii) Firm performance-related variables. Drawing on Cai et al. (2019), this paper 320 controls for both firm capital intensity (capital) and firm profit (profit) to account for the influence of factors such as 321 firm performance. Capital intensity is expressed as the ratio of the firm's fixed assets to total assets; corporate profit 322 is expressed as the logarithm of the firm's total profit. (iv) Relevant variables at the city level. To account for the 323 possible effects of regional openness, the economic development level and industrial structure changes at the city 324 level on the SO 2 emissions of heavily polluting enterprises (Yu and Zhang 2017; Jin and Shen 2018), this paper 325 controls for foreign investment share (lncityfdi), per capita GDP (lnpgdp) and industrial structure (lndustry). The 326 foreign investment share is the ratio of the total output value of foreign-invested industrial enterprises to the total 327 industrial output value of the region, GDP per capita is the logarithm of GDP per capita at the city level, and the 328 industrial structure is expressed as the share of the secondary industry in GDP at the city level. 329

Main results 340
The results of the baseline regression of the effect of the LCPC on SO 2 emissions are shown in Table 2.
2 is 341 the explanatory variable. Column (1) shows that the coefficient of the core explanatory variable is 0.08 and 342 significant at the 1% confidence level after we add only the core explanatory variable DID and the two-way year and 343 region fixed effects, indicating that the low-carbon pilot reform increases the SO 2 emissions of heavily polluting 344 enterprises in the jurisdiction by 8%. The coefficient of the core explanatory variable is 0.138 and significant at the 345 1% confidence level after we add the firm-level control variables (firm size, age, capital intensity, and profit) in 346 column (2), indicating that the low-carbon pilot reform increases the SO 2 emissions of heavily polluting firms in the 347 jurisdiction by 13.8% after the firm-level variables are controlled for. Column (3) further controls for three 348 indicators reflecting regional economic development (the foreign investment share, GDP per capita and industrial 349 structure of prefecture-level cities), and the coefficient of the core explanatory variable is 0.143 and significant at the 350 1% confidence level, indicating that the low-carbon pilot reform increases SO 2 emissions among heavily polluting 351 enterprises in the jurisdiction by 14.3% after firm-and prefecture-level variables are controlled for. Overall, this 352 indicates that the LCPC is significantly and positively related to the SO 2 emissions of heavily polluting enterprises, 353 indicating that hypothesis 1 is valid. 354 An important assumption required for the policy assessment using the multiperiod double-difference method is that 364 the time trends of the treatment and control groups would have been the same in the absence of the policy shock, 365 and thus, a parallel trend test of this assumption is required. For this purpose, we set up the following econometric 366 model: 367 ( 2 ) = + 2 −4 + 3 −3 +··· + 9 4 368 + + + + (8) 369 In the model, ± is a series of dummy variables, − takes the value of 1 when the treatment 370 group is in year j before the low-carbon pilot reform, and + takes the value of 1 when it is in year j after the 371 low-carbon pilot reform; otherwise, ± takes the value of 0. We take the year before the low-carbon pilot 372 reform as the reference category for the coefficient of ± in the regression. This coefficient indicates whether 373 there is a significant difference in the trend of SO 2 emissions between the treatment and control groups of 374 enterprises in year j before and after the low-carbon pilot reform in comparison with this difference in the control 375 group. To represent the estimation results visually, we present the trend of the coefficient of ± in Fig. 1, with 376 the horizontal axis indicating the years before and after the distance from the pilot and the vertical axis indicating the 377 magnitude of the estimated value. 378 From Figure 1,it can be seen that the coefficients of DID are not significant when j=-4, -3, -2, and -1, which 379 means that there is no significant difference in the trend of SO 2 emission changes of enterprises in the treatment and 380 control groups before the low-carbon pilot reform, so the hypothesis of parallel trends cannot be rejected. In the time 381 after the low-carbon pilot reform, the coefficient of + on enterprise SO 2 is significant at the 1% level from 382 the year of reform, which means that the low-carbon pilot reform intensifies enterprise SO 2 emissions basically 383 without a time lag and the effect can last for quite a long period of time. 384

Controls for potential omitted variables 396
Although we have included firm and year fixed effects and controlled for key indicators at the prefecture level, there 397 is still a possibility of omitted variable bias. Therefore, the firm-level variable corporate indebtedness (lndebts) is 398 added to the basic measurement equation. Corporate indebtedness reflects the market's evaluation of a firm's 399 creditworthiness (Meuleman and De Maeseneire 2012), and a moderately indebted operation allows firms to have 400 more abundant funds for activities such as technical equipment improvement and process upgrades. This indicator is 401 measured in this paper by the logarithm of the firm's loan amount to total assets ratio in the current year. We further 402 consider the return on total assets (ROA) of the enterprise, expressed as the ratio of enterprise net profit to total 403 assets. 404 Referring to the method of Xu and Cui(2020), we further add the following prefectural city-level variables: 405 the level of financial development (Credit), measured by the ratio of total loans from all financial institutions in the 406 region to regional GDP; the level of infrastructure (Infrastructure), measured by the number of telephone 407 subscribers; and the fiscal expenditure of the prefecture-level city (Fiscal), measured by the current year's fiscal 408 expenditure. All variables are taken as natural logarithms, except for the level of financial development. The results 409 in row (2) of Table 3 show that the regression coefficients of DID change very little in comparison to those in Table  410 2 after we control for municipality-and firm-level variables, indicating that these potential omitted variables do not 411 impact the basic findings. 412 413

Impact of the LCPC on total S 2 and C 2 emissions at the municipal level 414
A potential limitation of using firm-level data is that we can only observe the impact on existing firms. However, 415 environmental regulations may also lead to closures and entry restrictions among industrial firms if the cost of 416 enhanced environmental regulations is so large that firms cannot continue to be profitable . 417 Therefore, this paper further collects municipal-level data for the analysis, and the estimation results are presented in 418 row (3) of Table 3. The LCPC has a significant effect on SO 2 and CO 2 emissions, the coefficients of the double-419 difference term of SO 2 emissions in prefecture-level cities are all significantly positive at the 1% level, and the 420 coefficients of the double-difference term of CO 2 in prefecture-level cities are all significantly negative at the 1% 421 level. This indicates that the LCPC decreases CO 2 emissions but increases SO 2 emissions. This result is consistent 422 with the previous analysis. Therefore, the results of the firm-level analysis are reasonable. 423 , on the basis of which we construct a proxy variable for total corporate environmental protection input. In this 445 paper, the provinces and cities included in the scope of the first two rounds of low-carbon pilot projects are used as 446 the treatment group, and the remaining provinces and cities are used as the control group. To analyze the mechanism 447 whereby the LCPC influences enterprise production, we take the increase in the amount of end-of-pipe equipment to 448 control CO 2 and SO 2 emissions is taken as the proxy variable for enterprise capital investment to control CO 2 and 449 SO 2 emissions, and enterprise output is the proxy variable for total enterprise environmental protection input. The 450 specific model settings are as follows: 451 ln ( ) = 1 + 2 + 3 + 4 + 5 + + +  Table 4 (1), we find that the coefficient of total environmental protection investment of heavily 457 polluting enterprises in pilot areas after the implementation of the LCPC is 0.021 and significant at the 1% level, 458 indicating that the low-carbon pilot reform increases the total environmental protection investment of heavily 459 polluting enterprises in the jurisdiction by 2.1%. From Table 4 (2), we find that the reform causes a significant 460 increase of 4.5% in capital investment for CO 2 treatment by heavily polluting enterprises in the jurisdiction; this 461 figure is higher than the growth rate of total environmental protection investment. From Table 4 (3), we find that the 462 reform does not significantly increase capital investment in SO 2 treatment by heavy polluters in the jurisdiction. 463 Under normal circumstances, the growth rates of SO 2 -and CO 2 -related capital investment and total environmental 464 protection investment are similar; however, implementation of the LCPC makes the growth rate of CO 2 -related 465 investment much higher than that of total environmental protection investment, crowding out SO 2 -related 466 investment, so that the latter does not increase significantly. This naturally leads SO 2 emissions to increase. Thus, 467 hypothesis 2 is verified. 468

Impact of the low-carbon finance policy 472
The low-carbon pilot regions have all deployed low-carbon financial policies, aiming to provide financial support 473 for the low-carbon transition in the pilot regions. Such policies can facilitate financing for enterprises (Xu and Cui 474 2020) and alleviate their financial pressure in the process of managing CO 2 emissions, which in turn encourages 475 enterprises to increase their financial investment in managing CO 2 but reduce their investment in SO 2 476 management. Here, overall credit at the municipal level is used as a proxy variable for low-carbon financial policy 477 to test whether implementation of the LCPC leads enterprises to increase their investment in CO 2 treatment through 478 financial policy and produce a crowding-out effect on investment in SO 2 treatment. In this paper, on the basis of 479 model (9), we take the increase in the amount of end-of-pipe equipment for CO 2 and SO 2 treatment as the proxy 480 variable for the increase in enterprise financial investment in CO 2 and SO 2 treatment and add the loan variable 481 lnloanct at the municipal level to construct a triple-difference model as follows: 482 ( ) = + 1 * + 2 * + 23 * 483 ( 1 ) refers to the 486 logarithm of the amount of CO 2 equipment used by enterprise i in year t; if j=2, ( 2 ) refers to the logarithm 487 of the amount of SO 2 equipment used by enterprise i in year t. The regression results are shown in Table 5. The 488 coefficient of the triple-difference term is significantly positive in Table 5 (1), which indicates that the LCPC leads 489 enterprises to increase their capital investment in CO 2 treatment through the corresponding financial policies; on 490 the other hand, Table 5 (2) shows that financial policies inhibit enterprises' capital investment in SO 2 treatment. The 491 possible reason is that the low-carbon financial policies proposed by the pilot regions under their respective low-492 carbon planning programs mainly target green and low-carbon development, i.e., green industries, projects, and 493 production processes. The financial support for increased inputs SO 2 control is insufficient. At this point, 494 hypothesis 3 is verified. 495 Table 5 environmental regulations than state-owned enterprises. Therefore, here, the overall sample is divided into three 518 subsamples (state-owned enterprises, private enterprises and foreign enterprises) and the benchmark model re-519 estimated to further investigate whether the LCPC produces heterogeneous SO 2 emission effects for different types 520 of enterprises. 521 The estimated results are shown in Table 6. The double-difference term coefficient is significantly positive 522 in the subsample of state-owned enterprises corresponding to column (1); the double-difference term coefficient is 523 significantly positive for the private enterprises in column (2), but the rate of increase is much lower than that in 524 state-owned enterprises. In addition, the coefficient of the double-difference term is not significant for the subsample 525 SOEs' SO 2 emissions are more likely to be exacerbated by the LCPC than SOEs' in high-pollution industries. 539

Heterogeneity by region 542
Considering that the economic development conditions and industrial bases of each region differ greatly, the LCPC 543 may have heterogeneous effects on enterprise emissions across regions, and thus, we divide the sample into eastern, 544 central and western regions. The results in columns (1)-(3) of Table 7 show that the coefficients of DID are 545 significant at 0.105, 0.178 and 0.274, respectively; i.e., the pilot LCPC reform has a significant effect on enterprise 546 emissions in the east, central region and west of the country. The effect gradually increases from east to west, due to 547 the relatively greater development and stronger business operation capacity in the east, stronger governance capacity 548 of the eastern government, and better policies under the low-carbon pilot reform. The effect of the LCPC in the 549 central and western parts is relatively worse. 550 Fiscal decentralization also has implications for environmental regulation. Fiscal decentralization gives local 556 governments fiscal autonomy and a "residual claim" on revenues so that they can implement public policies that suit 557 their interests relatively independently to achieve their policy targets for CO 2 reduction. Fiscal decentralization 558 gives local governments the right to dispose of resources to ensure the effectiveness of incentives in political 559 promotion tournaments (Zhang 2016). Because the LCPC also involves assessment of local governments, LCPC 560 regional governments have incentives to use the fiscal autonomy granted by fiscal decentralization to meet carbon 561 targets, resulting in fiscal support that inhibits control of SO 2 . Fiscal policy focused on reducing CO 2 may crowd 562 out enterprises' SO 2 -related investment, which in turn inhibits enterprises' management of SO 2 . In short, in low-563 carbon pilot regions, because of the pressure of performance assessment, local governments are more willing to 564 adopt fiscal tools to reduce CO 2 emissions, which suppresses fiscal support for SO 2 treatment. The higher the 565 degree of fiscal decentralization, the greater is the fiscal autonomy of the region and the fiscal support for reducing 566 CO 2 emissions, which in turn discourages enterprises from investing in SO 2 control. 567 The existing literature disagrees about how to measure fiscal decentralization, using three main kinds of 568 indicators: expenditure indicators, revenue indicators and fiscal autonomy indicators. This paper draws on the 569 approach of Guo et al. (2020) to construct fiscal decentralization (FD) indicators for prefecture-level municipalities. 570 The higher is the degree of fiscal decentralization, the greater the fiscal autonomy of the region, the greater the fiscal 571 support for reducing CO 2 emissions, and the greater the crowding-out effect on the enterprises' investment in 572 managing SO 2 emissions. In this paper, based on model (9), we take the increase in the amount of end-of-pipe 573 equipment to control CO 2 and SO 2 as proxy variables for enterprises' inputs to control CO 2 and SO 2 and add the 574 fiscal decentralization variable FD at the municipal level to construct a triple-difference model as follows: 575 where F is the fiscal weight of municipality c in year t. ( ) is the logarithm of the amount of equipment 578 in enterprise i in year t. If j=1, ( 1 ) refers to the logarithm of the amount of CO 2 equipment in enterprise i in 579 year t. If j=2, ( 2 ) refers to the logarithm of the amount of SO 2 equipment in enterprise i in year t. The 580 regression results are shown in Table 8. The coefficient of the triple-difference term is significantly positive in 581 column (1), which indicates that by enhancing financial and taxation support at the municipal level, the LCPC leads 582 enterprises to increase their investment in CO 2 control; on the other hand, it can be seen from Table 8 (2) that the 583 financial and taxation policies do not prompt enterprises to significantly increase their investment in SO 2 control. 584 The possible reason is that the fiscal support policies proposed by the pilot regions in their respective low-carbon 585 planning programs target mainly green and low-carbon development, i.e., green industries, projects and production 586 processes. The fiscal and taxation policies to boost SO 2 inputs are not strong enough. 587 LCPC's influence on enterprise inputs into CO 2 and SO 2 management? In this paper, an interaction term between a 593 local market segmentation indicator and the LCPC indicator is introduced into model (9) to test this conjecture, and 594 a triple-difference model is constructed as follows: 595  Table 9. The coefficient of the triple-difference term is not significant in 599 column (1) and significantly negative in column (2), which indicates that the LCPC inhibits enterprise inputs into 600 SO 2 control through the mediating effect of market segmentation, exacerbating enterprises' SO 2 emissions. The 601 possible reason is that LCPC has different assessments of local governments' efforts to control CO 2 and SO 2 602 intensity, and local governments have more incentives to suppress SO 2 control inputs through market segmentation. 603 This paper focuses on the impact of the LCPC on the SO 2 emissions of heavily polluting enterprises. The findings 607 include the following: First, the LCPC has significantly exacerbated SO 2 emissions among heavily polluting 608 enterprises in the pilot areas, and the environmental treatment effect of the LCPC needs to be improved. Second, the 609 main transmission mechanism is the loan support provided through low-carbon financial policies under the reform 610 for the treatment of CO 2 inputs of heavily polluting enterprises in pilot areas, which inhibits support for SO 2 611 treatment inputs. The LCPC has a crowding-out effect, with CO 2 inputs displacing SO 2 inputs in high-pollution 612 industries in the pilot areas, which in turn has increased enterprises SO 2 emissions. Third, the LCPC has 613 significantly aggravated the SO 2 emissions of enterprises across the eastern, central and western regions, on the one 614 hand, and private and state-owned enterprises, on the other, with an increasing trend across these two sets of 615 subsamples. Fourth, it is further found that fiscal decentralization and the market segmentation resulting from fiscal 616 decentralization mediate the effect on enterprise CO 2 control and inhibit inputs into SO 2 control. 617 The findings of this paper have the following four policy implications. 618 First, the empirical results of this paper prove that the LCPC increases the SO 2 emissions and has a 619 negative effect on the clean production of heavily polluting enterprises; thus, the ecological and environmental 620 management effect of the LCPC needs to be improved. In the past 20 years, the role of low-carbon technology in 621 promoting economic and social change has become increasingly significant, and many cities around the world have 622 constructed low-carbon zones as an important means of enhancing the competitiveness of cities and even countries. 623 Compared with those of developed countries, the legal and institutional development of developing countries is 624 weaker, and area-based environmental policies often face greater obstacles and difficulties at the implementation 625 level. The results of this paper suggest that the effectiveness of low-carbon policies, a type of area-based 626 environmental policy, needs to be improved in the largest developing countries, and the findings of this study are 627 useful for us to re-examine the effectiveness of enterprise environmental governance under the LCPC. 628 Second, the results of the mechanism analysis suggest that the loan support provided under the policy for 629 CO 2 treatment inputs in the pilot areas for heavy polluters inhibits loan support for SO 2 treatment inputs. The 630 growth rate of investment in CO 2 mitigation among heavy polluters in the pilot areas is much higher than that in 631 total environmental protection inputs, but the investment growth rate of SO 2 inputs does not increase significantly. 632 This suggests that the design and planning of low-carbon policies should include clearer and more comprehensive 633 planning and support for innovation and technological upgrading to achieve synergistic management of the 634 ecological environment and climate change so that these enterprises can achieve the goal of reducing both 635 greenhouse gas and pollution emissions. 636 Third, the results obtained based on the heterogeneity analysis show that the LCPC's effects vary greatly by 637 geography, ownership type and level of fiscal autonomy. To strengthen the ecological environment, we should use 638 flexible and appropriate environmental regulations to give firms continuous innovation incentives. This paper finds 639 that the effect of the LCPC varies among firms by ownership, geographical area and fiscal autonomy level: this 640 difference also reflects that the government needs to make environmental policies with greater consideration of 641 different firms characteristics. The design of the LCPC system should take into account these aspects, and in 642 addition to creating a level playing field for less developed regions and nonstate enterprises, the monitoring 643 mechanism can be designed to apply greater compliance pressure on less developed regions, regions with greater 644 fiscal autonomy, and state-owned enterprises. 645 Fourth, based on further research results, fiscal decentralization and the associated mediating effect of 646 market segmentation promote inputs into enterprise CO 2 governance and inhibit inputs into enterprise SO 2 647 governance. We should increase marketization in the economy, reduce unnecessary government intervention, and in 648 general leverage the role of the market in resource allocation. 649 The findings of this paper imply that the LCPC has a negative effect on cleaner production among heavily 650 polluting enterprises and that the ecological and environmental management effect of the LCPC needs to be 651 improved. China's sustained high economic growth for more than 40 years has brought about severe resource and 652 environmental pressure; alleviating this pressure requires continuous efforts and reforms, and the LCPC is one of the 653 flagship efforts among many environmental reforms. A scientific and systematic assessment of the effectiveness of 654 the regional-based LCPC provides experience and inspiration to formulate relevant environmental pollution 655 prevention and control policies in developing countries in the short term; in the long term, it is of great practical 656 significance to help developing countries to build ecological civilization as a millennium plan for sustainable 657 development. 658