Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises.

This study considers the implementation of the \"Broadband China\" strategy as an exogenous policy shock and examines the impact of network infrastructure construction (NIC) on the low-carbon innovation (LCI) of enterprises and its underlying mechanisms by using a progressive difference-in-difference model based on the data of Chinese listed enterprises from 2009 to 2020. This study finds that NIC can improve the LCI of enterprises. After the elimination of the sample selection bias and selection of the urban slope as the exogenous instrumental variable, the conclusions remained robust. The results of the mechanism test show that upgrading the human capital level, reducing transaction costs, and alleviating financing constraints are the three important paths through which NIC can help enterprises improve their LCI level. The heterogeneity analysis determines that NIC has considerable comparative advantages for enterprises with executives who have a financial background and enterprises with high knowledge stock. In addition, LCI improvement can further enhance enterprise value. The research conclusions can broaden the microscopic research perspective of enterprise transformation and upgrading theory and provide reliable empirical evidence for China\'s low-carbon economic transformation.

Low-carbon innovation plays an essential role in carbon reduction worldwide. This study investigates the nexus between low-carbon innovation, economic growth, and carbon emissions by the dynamic spatial Durbin model from 2007 to 2020. First, the Moran index results verify the provincial spatial agglomeration of carbon emissions. High-emission provinces concentrate in major economic zones and energy extraction areas. Second, the effect decomposition results show that long-term and short-term effects are consistent. Low-carbon innovation has a significant mitigation effect on carbon emissions in local regions, which effect, however, is not significant in the adjacent areas. The environmental Kuznets curve hypothesis is validated locally, but all provinces and cities have not reached the inflection point of the environmental Kuznets curve, and the linkage effect in adjacent regions remains insignificant. The above results have been tested to be robust. Third, the results of the mechanism analysis show that environmental policies, absorptive capacity, and financial development play a moderating role in the relationship between low-carbon innovation and carbon emissions. Finally, the heterogeneity test showed significant differences between Eastern, Central, and Western. The direct effect of low-carbon innovation exists in Eastern and central regions; the spillover effect of low-carbon innovation is only in the eastern region. In addition, corresponding measures are proposed based on the conclusions.

Carbon dioxide (CO2) emission reduction has become an important concern worldwide. During the past century, human activities have been a significant cause of the increase in the level of greenhouse gases. Past research mainly focuses on evaluating the nexus between unidimensional population factors and CO2 emissions, while few prior studies in a developing country have reported the impact of multidimensional demographic factors on CO2 emissions. As an initial attempt, this study investigates the short- and long-run associations between population factors, low-carbon innovation, and carbon dioxide emissions (CO2) for a panel consisting of 285 cities by employing the pooled mean group (PMG) estimator under the framework of the panel autoregressive distributed lag (ARDL) model. Our main findings are as follows: (1) Population size and population density could increase CO2 emissions, while population quality and low-carbon innovation were essential factors that alleviate carbon emission pressure in the long run. (2) Economic development, foreign direct investment, and industrial development were found to be factors causing the increase in carbon emissions. (3) The split-sample analysis demonstrated that the improvement of population quality still has a positive and significant long-run effect on environmental quality. Simultaneously, low-carbon innovation could realize the enormous dividends of carbon emission reduction in the long run, especially in existing relatively larger CO2 emission areas. Finally, the paper presents important policy implications.

The carbon emission trading system (CETS) is an important market-oriented policy tool for the Chinese government to solve the problem of high emissions and achieve the growth of green total factor productivity (GTFP). This study makes up for the neglect of the spatial effect of CETS policy in previous studies and adopts the spatial difference-in-differences (DID) Durbin model (SDID-SDM) method of two-way fixed effects to scientifically identify the direct and spatial effects influencing the mechanisms and heterogeneity of CETS on urban GTFP based on the panel data of 281 cities in China from 2004 to 2017. It found that China\'s CETS significantly improved the GTFP of pilot cities but produced a negative spatial siphon effect that restricted the growth of GTFP in surrounding cities. Benchmark results are robust under the placebo test, the propensity score matching SDID (PSM-SDID) test, and the difference-in difference-in-differences (DDD) test. The mechanism analysis shows that the CETS effect is mainly realized by improving energy efficiency, promoting low-carbon innovation, adjusting the industrial structure, and enhancing financial agglomeration. In addition, we find that policy effects are better in cities with high marketization, strong monitoring reporting and verification (MRV) capabilities, high coal endowment, and high financial endowment. Overall, China\'s CETS policy achieves the goal of enhancing GTFP but needs to pay attention to the spatial siphon effect. In addition, our estimation strategy can serve as a scientific reference for similar studies in other developing countries.

Low-carbon innovation can address both economic and environmental concerns; patterns of low-carbon innovation convergence can determine the effectiveness of mitigating the adverse consequences of climate change. Considering that economic openness has a huge impact on the development of innovation capability, this paper uses a conditional β convergence model to examine the convergence of low-carbon innovation in Chinese manufacturing industry and its relationship with economic openness. We incorporate the spatial spillover effect into the convergence function by constructing spatial error model, spatial lag model, and spatial Durbin model. Based on a panel data set of 30 Chinese provinces over the period 2004-2016, the results show that low-carbon innovation in Chinese manufacturing industry has a strong feature of conditional β convergence. The convergence rate of low-carbon innovation is slightly slowed down by economic openness, and the main reason is that the spillover effect is weak and the convergence rate is slow in lower open areas, so the convergence rate of the whole country is slowed down by that of the lower open areas. Although the economic openness in adjacent areas can contribute to the development of local innovation ability, but generally speaking, economic openness in local areas takes a stronger effect in promoting the convergence of low-carbon innovation than that in adjacent areas. The findings have important policy implications as they suggest the need for a more equal degree of economic openness among Chinese provinces to speed up the convergence of low-carbon innovation.

Climate change caused by carbon emissions has a strong influence on the economy and human society. Though numerous previous studies have emphasized the importance of low-carbon innovation on curbing or mitigating carbon emissions, not much attention has been given to the reverse effect. We used a panel of 285 Chinese prefecture-level cities from 2005 to 2016 and Cooperative Patent Classification (CPC)-Y02 patents as low-carbon innovation indicators. The results show that the increasing carbon emissions accelerate cities\' low-carbon innovation in China, and the predicted effect varies across low-carbon innovation types. As carbon emissions rise, more low-carbon innovation will occur in activities with higher carbon emissions. Besides, we explore environmental awareness as the mediation channel for carbon emissions to impact low-carbon innovation. With the help of media, government, and enterprises, the growing carbon emissions promote public environmental awareness and change consumers\' behaviors, motivating companies to speed up low-carbon innovation.