Innovation is the first driving force for development, and green innovation efficiency (GIE) plays a very important role in regional sustainable development. Data from 31 provinces and cities in China from 2011 to 2020 were used to select the proportion of energy saving and environmental protection costs in GDP as the green financial value, and the proportion of industrial pollution control input in GDP as the environmental regulation index. Green innovation efficiency is measured from two aspects of input and output by DEA method, and carried out for 31 provinces and cities in three regions. Using the DEA-Malmquist index to measure regional green innovation efficiency, the results show that the green innovation efficiency in three regions basically presents an upward trend, but the upward trend of green innovation efficiency is different between the three regions. A Tobit regression model is constructed to explore the impact of green finance and environmental regulations on the green innovation efficiency in these three regions. Research indicates that environmental regulations, the proportion of output value of tertiary industry in GDP, industrial structure, and foreign direct investment have significant impacts on the green innovation efficiency in all regions. Green finance, industrial structure, and power consumption have a significant impact on the green innovation efficiency in eastern China. Industrial structure has a significant impact on green innovation efficiency in central China, while power consumption and industrial structure have a significant impact on green innovation efficiency in western China. Therefore, each region needs to improve the standard of environmental regulation innovation, and introduce and use foreign investment in a scientific and reasonable way so as to promote the improvement of industrial infrastructure.

In recent years, the impact of global climate change has increasingly revealed that energy transformation has become an indispensable part of achieving carbon neutrality. Thus, the relationship between energy transformation and economic growth has become the focus of academic attention. This study examines energy transition issues by using the panel threshold method. It explores the nonlinear impact of renewable energy consumption on economic growth, identifies various factors that lead to this nonlinear impact, and verifies its threshold effect. A comprehensive analysis reveals the following. (1) Overall, renewable energy consumption inhibits real gross domestic product (GDP) growth, but, in the long run, the negative impact becomes positive. (2) The threshold effect of energy consumption intensity (EI) is significant, with a threshold value of approximately 3.213. This means that when EI ≤ 3.213, renewable energy consumption promotes economic growth. However, EI > 3.213 indicates that this impact is significantly negative, which means that advancing the energy transition at this time may occur at the expense of real GDP growth. (3) There is also a significant threshold effect in energy transformation, with a threshold value of approximately 6.456. Similarly, when energy consumption transition (ET) ≤ 6.456, renewable energy consumption dampens real economic growth, and the economic cost of promoting renewable energy consumption is greater at this time. Alternatively, when ET > 6.456, this impact is significant at the 1 percent level and significantly positive. (4) There is also a significant threshold effect for emerging technologies, with a threshold value of approximately 1.367. When ET ≤ 1.367, renewable energy consumption dampens real economic growth, and the economic cost of promoting renewable energy consumption is greater. When ET > 1.367, the impact is significantly positive at the 1% level. To promote the positive development of economic growth, climate change, and energy transition, the nonlinear relationship studied in this paper can fill the gaps in existing research in theory and provide a theoretical basis for the government to adopt different policies at different stages of the energy transition to lay the foundation for improving global climate change in practice.

This paper selected the data from 2010 to 2020 to measure the carbon emissions of the logistics industry in different regions of China, decomposed the influencing factors of carbon emissions in China’s logistics industry based on the LMDI model, and, finally, conducted a decoupling analysis of carbon emissions and the development of the logistics industry. The conclusions are as follows: (1) China’s carbon emission levels vary greatly from region to region, with the highest distribution pattern in the east and the lowest in the west, while the growth rate in the east is also the highest. (2) The level of economic development has the greatest impact on carbon emissions, and it has the effect of promoting carbon emissions in three regions; logistics development effects have the characteristics of first driving and then restraining emissions in the three major regions. The effect of energy intensity has great volatility. The effect of intensity in the eastern region dropped sharply in 2015, with negative effects after that year. Development of the logistics industry has limited the inhibition of carbon emissions in the central and western regions. Although the effect of the energy structure is negative, it failed the significance test. The effects of the energy structure began to show a downward trend in three regions after 2015. (3) The decoupling analysis showed that only 3 provinces are strongly decoupled, 20 provinces are weakly decoupled, and the regional carbon emissions are quite different.

From a global perspective, carbon emissions are a global problem that needs to be solved urgently. At present, 61% of countries have committed to achieving net zero emissions. Compared with industry and construction, the transportation sector has become the focus and challenge for countries to achieve carbon neutrality due to the characteristics of strong mobility, scattered emission sources, and complex social behaviors. Therefore, the issue of carbon emissions in the transportation industry has become the focus of academic attention. This paper first calculates the carbon emission efficiency (CEE) of the regional transportation industry through the super-efficiency SBM model and then evaluates its regional differentiation characteristics through the Theil index, which has important practical significance for reducing regional carbon emissions. The results show that the national transportation CEE average value is 0.612, a relatively low level. The spatial distribution of China’s transportation CEE shows an obvious characteristic of “east highest and west lowest”. The regional differences in the transportation industry CEE are larger than those between regions. The differences in the transportation industry CEE among the eastern, central, and western regions are on the downward trend as a whole, and intra-regional differences are greater than inter-regional. The intra-regional differences cause the overall differences in transportation industry CEE; the eastern region contributed the most to the Theil index, while the central contributed the least. The biggest factor affecting the transportation industry CEE is the regional energy structure, and the smallest factor is the per capita GDP. This research has important reference significance on the target of carbon neutrality.

The development of e-commerce plays a very important role in changing the production and operation mode, optimizing the allocation of market resources, promoting sustainable development, and ultimately achieving the goal of e-commerce poverty alleviation. Therefore, the efficiency of e-commerce poverty alleviation has become a focus of attention for both the government and academia. The authors of this paper selected the panel data of 30 provinces and cities in China from 2010 to 2021, in order to measure the poverty alleviation efficiency of e-commerce in each province and city. We used the Moran’s I index to measure its spatial correlation to verify the existence of its spatial effect; we then used the spatial Durbin model to analyze the spatial spillover effect in the efficiency of e-commerce poverty alleviation. The conclusions are as follows: First, there is a significant positive spatial correlation of the efficiency of e-commerce poverty alleviation among different regions in China. Moran’s I index exceeds 0.5, indicating that there is a significant spatial effect in the efficiency of e-commerce poverty alleviation, and the existence of its spatial effect is unavoidable in the empirical analysis. Secondly, from the perspective of the efficiency of e-commerce poverty alleviation in various regions of the country, the overall e-commerce poverty alleviation efficiency is not high, and there are large differences among regions. The regions in which efficiency is higher include Tianjin, Beijing, and Shanghai. Regionally, the highest are in the east and the lowest are in the west. Secondly, from the decomposition of spatial spillover effects, the direct effects of each influencing factor are all positive. Only the financial development environment is less significant, and the indirect effects indicate that only four indicators have significant spatial spillover effects, of which the most significant is industrial agglomeration. The level of industrial agglomeration is not significantly related to the level of human capital, and there is a negative correlation between it and the efficiency of e-commerce poverty alleviation. The authors studied the poverty alleviation efficiency and spatial spillover effect of China’s regional e-commerce from the perspective of sustainable development, which is beneficial to China’s regional poverty alleviation results, providing practical guidance and decision-making reference for implementing differentiated coping strategies in different regions. The research complements, improves, and expands the research content in this field.

Environmental regulation is an important guarantee to realize the balanced development of economy and environment, and is the policy guidance of the government to the resource utilization of companies. Environmental regulation will make companies increase costs, and also promote companies to explore technological innovation. How to reverse the negative cost effect of environmental regulation and improve financial performance is a problem that the government and companies need to think about together. Based on the data from 2015 to 2019 of China’s heavy-polluting industry of listed companies, this paper analyzes the impact of environmental regulation on financial performance. The results show that: (1) environmental regulation inhibits firms’ short-term and long-term financial performance. Environmental regulation has formed the negative effect of cost crowding out; (2) environmental regulation positively affects technological innovation. Environmental regulation can stimulate companies to actively explore technological reform to cope with the requirements and restrictions of environmental regulation policies; (3) technological innovation is not significantly negatively correlated with corporate financial performance. Technological innovation input cannot directly lead to the improvement of financial performance in the current period; and (4) technological innovation positively mediates the relationship between environmental regulation and financial performance. That verifies a weak “Porter Hypothesis”. “Innovation compensation effect” exists, and technological innovation can partially offset “cost crowding out effect” and slow down the inhibition relationship of environmental regulation on financial performance, but the innovation effect does not offset the environmental cost. According to the research results, environmental regulation still has a negative effect on the financial performance of listed companies in China’s heavy-polluting industries. The government should formulate strict and stringent environmental regulation policies, guide companies to rationally use resources and protect the environment. Instead of passively responding to policies, companies should actively seek new technologies and alternative sources of energy. Companies try to amplify the compensation effect of technological innovation, reduce the cost crowding out effect of environmental regulation, and improve their value.