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Renewable energy is not only an efficient way to ensure energy independence and security but also supports the transition to a low carbon economy and society. The progress of renewable energy technological innovation is an important factor that influences the development of renewable energy. An in-depth analysis of the driving factors that influence this progress is crucial to China's energy transition. Based on Chinese provincial data over 2000–2015 and panel data models, this paper regards the CO 2 emissions as climate change and explores the response of renewable energy technological innovation to intensive CO 2 emissions. We also analyze the effect of the driving factors such as energy price and R&D investment on this innovation process. The main conclusions drawn are: (1) There are significant differences in technological innovation levels across China's provinces. (2) We observe that the intensive CO 2 emissions have promoted renewable energy technological innovation level, meaning that innovation process responds actively to climate changes. (3) R&D investment from government and enterprise both are conducive for promoting the innovation level. (4) Energy price has an insignificant effect on innovation in renewable energy technologies and we attribute this to the unreasonable energy price mechanism. This paper provides clear evidence for understanding the role of innovation on climate change. • The drivers of the renewable energy technological innovation are explored. • There are significant differences in technological innovation across provinces. • Renewable energy technological innovation responds actively to climate changes. • R&D investment is conducive for promoting the technological innovation levels. • The government should give full play to the role of the energy price mechanism. [ABSTRACT FROM AUTHOR]

This paper examined the economic impacts of China's emissions trading system (ETS), implemented in 2013, in terms of gross domestic product (GDP) and per capita GDP. The methodology drew on a difference-in-differences (DID) method using dynamic panel data for 30 provincial regions. The estimation results show that implementing an ETS policy has statistically significant negative effects on economic growth. When considering the additional control variables, these effects are still statistically significant. Furthermore, these negative impacts increase over time with respect to both GDP and per capita GDP. The counterfactual tests confirm the robustness of our DID estimation results. The specific influence mechanism of China's ETS policy was finally discussed. Our findings provide a quantitative decision-making support for popularizing ETS policy in China. Based on our DID model, empirical investigations of the ETS policy, especially concerning economic growth in other regions would produce prolific contributions to the literature. [ABSTRACT FROM AUTHOR]

Recently the expansion of urban-rural income inequality and the rapid growth of fossil-energy consumption have become two significant challenges China should address to achieve its ambitious goals. For the first time, this study uses the Chinese provincial panel data during the period of 1996–2013 to perform a quantitative analysis on the relationship between the urban-rural income gap and the per capita electricity consumption. Fully considering the potential endogeneity problem, this paper uses the orthogonal-difference Generalized Method of Moments (GMM) as the benchmark estimation method. To ensure the robustness of the estimations, the Theil index and per capita urban-rural income are utilized to measure the urban-rural income gap. The estimation results indicate that the influences of urban-rural income inequality on electricity depend on the income level. At the current stage of economic development, the income disparity has significantly negative impacts on provincial per capita electricity consumption. Moreover, there is also strong evidence for the existence of the inverted U-shaped relationship between per capita electricity consumption and GDP per capita. Furthermore, other economic and social factors, including the enhancement of urbanization and industrialization levels, the adjustment of the population structure, and the development of import-export trade may also promote electricity consumption. [ABSTRACT FROM AUTHOR]

Carbon footprint (CF) and inequality reductions were two of the most important sustainable development goals. However, there was a lack of understanding of the latest equality of carbon footprint across regions. Based on the multi-regional input-output table and the China Family Panel Studies survey data of 17 provinces, this paper examined the evolution of carbon footprint and inequality in China. The results showed a stark disparity between regions and a declined inequality in the last few years. Specifically, the CF per capita in northern provinces (Liaoning, Shanxi, Heilongjiang, Shandong and Shaanxi) exceeded the average level in 2017. From 2015 to 2017, the carbon inequality showed a downward trend, with a national CF-Gini coefficient decrease from 0.31 to 0.28. Meanwhile, the gap between CF per capita in urban areas and rural areas was declining, with a growth rate at 18% and 32% from 2015 to 2017, respectively. In addition, CF per capita from various consumption categories indicated that household equipment and daily necessities, residential, transportation and communication were the three most important sources, with a downward trend from 2015 to 2017. This study would provide policymakers with deep insights into policies to achieve just transition. [ABSTRACT FROM AUTHOR]

The paper investigates how climate changes and socioeconomic transformations affected grassland productivity from 2000 to 2017 in counties in Inner Mongolia of China. The grassland productivity and climatic change variables included 162 readings (9 periods in the growing season × 18 years), while ten socioeconomic variables contained 18 yearly readings. The mismatch between the environmental observations and socioeconomic data compounds quantitative analysis of causal relations between them. We applied several time-series processing methods to generate five datasets. We fit them with conventional, dependent-lagged, and dynamic-lagged panel regression models to investigate which combinations augment causal relationships between grassland productivity, climate, and socioeconomic changes. The results confirmed that the conventional model with the original data failed to reveal causal relationships between these factors. The panel regression models with the original data tended to intensify the causal effects of climate factors on grassland productivity. The models with the transformed datasets disclosed more socioeconomic variables showing causal relationships with grassland productivity. The dependent-lagged model with the empirical mode decomposition transformed data produced the highest R-squared value and lowest prediction error. GDP, state fixed asset investment, highway construction, grain plantation, and livestock density were primary factors affecting grassland productivity. In brief, the integration of time-series data mining and time-lagged panel analysis effectively unveiled the causes of grassland deterioration under climate and socioeconomic changes. [Display omitted] • Augment causality hidden in env-socio-economic systems with data assimilation. • Assimilate temporal environmental and socioeconomic data into compatible time-series. • Experiment with four groups of computational data mining methods on data assimilation. • Compare combinations of data assimilation methods and lagged panel regression models. [ABSTRACT FROM AUTHOR]

Climate change and global warming have become severe environmental issues of global concern, and many countries have promulgated renewable energy development planning to achieve the targets of carbon emission reduction and sustainable development. Accelerating the development of renewable energy technology innovation is one of the major measures to stimulate the utilization of renewable resources. This paper analyzes the impact of renewable energy technology innovation on carbon intensity in 30 Chinese provinces from 2000 to 2015. Using the recent panel estimation methods that take cross-section dependence and slope heterogeneity into account, the results show that for every 1% increase in the innovation level of renewable energy technology, carbon intensity was significantly reduced by 0.051%. Specifically, renewable energy technology innovation does not affect carbon intensity in the short term. But in the long term, the influences are negative and significant. Moreover, the environmental benefits are most evident in eastern China, while there is no significant influence on the western regions. Finally, this paper provides some policy implications to further discuss the impetus of renewable energy technology innovation. • Renewable energy technology innovation can significantly reduce carbon intensity. • The influences of renewable energy technology innovation will change over time. • There exist differences in the innovation effects in different Chinese regions. [ABSTRACT FROM AUTHOR]

Understanding the extent to which air pollution influences the process of attaining sustainable development goals (SDGs) may help to estimate the benefit of reducing air pollution for improving sustainable development and to evaluate the necessity and urgency of reducing air pollutants and improving air quality. In this paper, we use the latest calculation of the SDGs in China to construct provincial panel data and investigate the relationship between air pollution and Chinese SDGs. We find that reducing air pollution will not only improve the SDGs directly associated with the environment, such as SDG3 (good health and well-being) and SDG15 (life on land), but also has a close relationship with the SDGs of other dimensions, such as SDG4 (quality education), SDG9 (industrial, innovation and infrastructure), SDG12(responsible consumption and production), SDG16 (peace, justice and strong institutions), SDG17(global partnerships). Based on the actual reduction in industrial SO2 emissions and evolution of the SDGs in China, we estimate that the former contributes approximately 3.5%–12.3% of the actual change in these SDGs from 2005 to 2015. Our findings support that reducing air pollution benefits the progress of sustainable development in various ways, and the efforts of curbing air pollution and improving air quality are especially valuable for sustainable development. [ABSTRACT FROM AUTHOR]

This paper explores the historical relationship between weather fluctuations and mortality evolution in China. Using panel data on the annual provincial mortality rate and daily weather variables for the 1964–2008 period, we applied a dynamic model structure with sufficient controls to estimate the short-term effects of temperature shocks on China's mortality rate. Our main results show that an additional 1 °C rise in average temperature in a given year increases the annual mortality rate by an average of 3.2%. In addition, days with daily average temperatures exceeding 27 °C (30 °C) are associated with an increase in the annual mortality rate of nearly 0.1% (0.2%) or approximately 9,520 (19,040) additional deaths. In contrast, extremely cold days do not have these effects. By applying a hybrid model structure (the long difference approach) to examine the medium-term effects of temperature changes, we find that there is little evidence of overall adaptation to high temperatures when moving from the short term to the medium term. In addition, by introducing interactions between temperatures and potential modifiers such as access to doctors, hospital beds, air conditioning and refrigerators, we determined that only residential air conditioning played an important role in mitigating the temperature-mortality relationship. However, the penetration of air conditioning in China is still relatively low. These findings indicate that climate change could partially explain the historical phenomenon of China's rising mortality rate during the unprecedented economic boom experienced since the 1980s. • Increases in the number of extreme hot days increase annual mortality rate. • Little evidence of adaptation when moving from short run to medium run. • Residential air conditioning has played an important role in mitigating the temperature-mortality relationship. [ABSTRACT FROM AUTHOR]

As an economic means to cope with climate change, carbon trading has become an important measure to reduce carbon emissions in China. It has been over seven years since the initiation of seven pilot carbon markets, but has the implementation achieved the goal of controlling carbon emissions in the region? Are the effects really resulted from the carbon market other than other energy-saving and emission reduction policies? Based on the provincial panel data of China from 2005 to 2016 this paper conducts an empirical study on the emission reduction effects of pilot projects using the Difference-in-differences model (DID). The results show that: (1) The implementation of carbon markets in Beijing, Shanghai and Hubei exhibited a significant inhibitory effect on local carbon emissions, but a promoting effect in Guangdong, and no significant effect was found in Tianjin; (2) In terms of time lag effect, no significant inhibiting effect on the local carbon emissions is found during the first year of opening of Shanghai carbon market but the effect grew significantly from 2015 to 2016; while for Hubei and Beijing, significant effect was shown from the first year of running. Being an experimental program to see whether the carbon trading policy would work in China or not and if it does, under what conditions and settings the policy becomes effective, the 7 pilot markets provide important experiences and lessons to the upcoming nationwide market in China. Based on the empirical result that the carbon trading system in Beijing, Shanghai and Hubei effectively reduced carbon emissions, it is suggested that the national carbon market should actively learn from the experiences of these three pilot markets particularly. [ABSTRACT FROM AUTHOR]