*3.3. Control Variables*

There are many factors that affect industrial pollution. The core explanatory variable of this article is only to filter out variables that can promote land use transition from the perspective of innovation agglomeration. Therefore, control variables should also be selected from the following aspects to prevent endogenous problems caused by omission of important explanatory variables. (I) Per capita GDP (Per\_GDP). Buryn et al. found a close relationship between economic development and pollution emissions [51]. On the one hand, the higher the per capita GDP of a region, the higher the residents' requirements for the quality of life and living environment. On the other hand, companies that can support a high level of per capita GDP often have a higher rate of return on investment, which is difficult for traditional industrial companies to achieve. (II) Foreign direct investment (FDI). In the early period of reform and opening-up, China had cheap labour, raw materials and land, which was extremely attractive to foreign manufacturers. They can use cheap

production factors and perform corporate social responsibilities with low standards. However, with the increase in the cost of setting up manufactures in China and the Chinese awareness of environmental protection, the role of FDI in increasing industrial pollution emissions may weaken or even reverse [52]. This is not the focus of this article. However, it is undeniable that FDI in this study is a good control variable reflecting foreign influence. (III) Energy structure. Wang et al. found that there is a highly positive correlation between the proportion of coal energy and industrial pollution [53]. When the proportion of traditional coal energy drops, it indicates that the proportion of other relatively clean energy (wind power, hydroelectric power, etc.) in China has risen, thereby affecting industrial pollution emissions. Accordingly, this study uses the ratio of total electricity generation to total energy consumption to measure the energy structure. In addition, the converted standard coal coefficient (1.229 tonnes of standard coal/10,000 kWh) is used to convert the units of electricity generation into million tonnes of standard coal. Table 1 summarizes the indicator system.



Mean and Std. are both values in logarithmic form.

### *3.4. Data Resource*

Since 2006 (the starting point of the 11th Five-Year Plan), China has strictly controlled pollution emissions and strengthened its efforts to carry out green development. Therefore, this study uses the panel data of 30 provinces in China after 2006 (2006–2018) for research. In view of the integrity and availability of the data, the Tibet Autonomous, Hong Kong, Macao and Taiwan were excluded. All raw data comes from China Statistical Yearbook, China statistical yearbook on Science and technology, provincial statistical yearbook. Some of the missing data are supplemented using the moving average method.
