New insights to Pollution and Carbon Reduction Effects of China Pilot Carbon markets: Perspectives on Technological Progress and Corporate Disincentives

Panel data from 271 prefecture-level cities in China (excluding Tibet and Xinjiang owing to data availability) from 2006 to 2021 were utilized to empirically examine the effects of the pilot carbon market on pollution and carbon reduction. The sample encompasses both pilot and non-pilot areas to investigate how the carbon market influences the relative number of enterprises in pilot cities. The study period concludes in 2021 due to the launch of China's national unified carbon market in July 2021.

green technological innovation (Green), industrial structure upgrading (Ind), energy structure (Energy), and the relative number of enterprises (Firm) were selected as mediating variables. Green technological innovation is measured using the logarithmic value of green invention patent authorization. Industrial structure upgrading is quantified by the ratio of tertiary industry added value to secondary industry added value. The energy structure is quantified by the ratio of standard coal from the total electricity consumption and LPG gas supply to the total tons of standard coal in the whole society. The change in the relative number of firms is measured by the mean value of the number of firms. The detailed calculations are as follows:.
"Firm=" "x_it" /"μ"
"x_it" denotes the number of above-scale industrial enterprises in city i in year t. μ represents the mean value of the number of above-scale industrial enterprises across all cities from 2006 to 2021, indicating the city's number of enterprises relative to the national average.
Gdp: Logarithmic value of GDP
Fin: Ratio of loan balance of financial institutions to regional GDP at the end of the year
Struc: Ratio of secondary and tertiary sector output to GDP
Sci_edu: Logarithm of the ratio of education and science and technology expenditures to general government expenditures
Fdi: ratio of actual utilization of foreign capital to regional GDP.
The environmental pollution index comprises three indicators: sulfur dioxide, PM2.5, and Industrial fumes and dust emission. The improved entropy weight TOPSIS method is employed to construct a comprehensive environmental pollution index.

本研究采用2006年至2021年中国271个地级市的面板数据（因数据可得性限制，剔除西藏自治区与新疆维吾尔自治区样本），实证检验试点碳市场对污染减排与碳减排的影响效应。样本涵盖碳试点地区与非试点地区，以探究碳市场对试点城市企业相对数量的作用机制。鉴于中国全国统一碳市场于2021年7月正式上线，本研究的样本周期截至2021年。

选取绿色技术创新（Green technological innovation, Green）、产业结构升级（Industrial structure upgrading, Ind）、能源结构（Energy）以及企业相对数量（Firm）作为中介变量。其中，绿色技术创新以绿色发明专利授权量的对数值进行测度；产业结构升级以第三产业增加值与第二产业增加值的比值进行量化；能源结构以全社会总标准煤消耗量中，总电力消耗与液化石油气（LPG）供应折算的标准煤占比进行衡量；企业相对数量的变化以企业数量的均值表征，具体计算方式如下：
$$ ext{Firm} = frac{x_{it}}{mu}$$
其中，$x_{it}$代表第$t$年$i$市的规模以上工业企业数量，$mu$为2006年至2021年全样本城市规模以上工业企业数量的全国均值，用以表征该城市企业数量相对于全国平均水平的相对值。

控制变量设置如下：
- GDP：地区生产总值的对数值
- Fin：年末金融机构贷款余额与地区生产总值的比值
- Struc：第二、三产业产值占地区生产总值的比重
- Sci_edu：教育与科技支出占一般公共预算支出比重的对数值
- Fdi：实际利用外商直接投资与地区生产总值的比值

环境污染综合指数包含二氧化硫、PM2.5以及工业烟粉尘排放三类指标，本研究采用改进的熵权TOPSIS（Improved Entropy Weighted TOPSIS）方法构建综合环境污染指数。