Energy technological innovation and carbon emissions mitigation: evidence from China

Do energy technology innovations contribute to CO2 emissions abatement? A spatial perspective

With the rapid development of China's economy, its energy consumption and carbon emissions have ranked first in the world. The Chinese government has proposed to peak its carbon emission by 2030. In order to achieve this goal, the realization of a low-carbon economy through energy technology innovation, especially renewable energy technology innovation (RENTI) is necessary. Based on the panel data of 30 provinces in China from 2004 to 2018, this paper measures the relationship between RENTI and CO2 intensity. Therefore, empirical tests were performed using the augmented mean group estimation and pooled mean group-autoregressive distributive lag models to investigate the impact of RENTI on CO2 intensity. The results of the heterogeneity study showed that the impact of RENTI on CO2 intensity was the largest in the eastern region, followed by the central region. Although the influence of RENTI on CO2 intensity in the western region was negative, it did not pass the significance test. To reach its emission goal, based on regional characteristics, the Chinese government should increase the financial support for RENTI, implement differentiated RENTI policies, train RENTI talents, accelerate the development and transformation of renewable energy technology, implement energy substitution, and develop the rural renewable energy market.

The development of renewable energy is a strategic deployment to address climate change and ensure energy security. Frontier research generally focuses on the impact of renewable energy development and technological innovation on environmental protection and neglects the inequality in renewable energy technology innovation. This paper constructs a renewable energy technology innovation inequality index and analyzes the impact of digital finance on renewable energy technology innovation inequality using panel data from 30 regions in China from 2011 to 2018. The study found that digital finance can alleviate inequality in renewable energy technology innovation. The mechanism test results show that digital finance can optimize green credit allocation, promote technology flow, and optimize energy consumption structure, thus reducing inequality in renewable energy technology innovation. Further analysis shows that the differences in local government governance, environmental regulation, financial supervision, marketization, digital divide, and renewable energy category affect the relationship between digital finance and inequality in renewable energy technology innovation. This paper clarifies the relationship between digital finance and inequality in renewable energy technology innovation and provides new ideas for renewable energy technology innovation.

The characteristics of common prosperity include harmonious relationships between humans and the environment, as well as sustainable economic and social growth. The process of achieving common prosperity will necessarily have an impact on carbon emissions. In this article, panel statistics collected from 30 Chinese provinces and cities between the years 2006 and 2020 are utilized to assess the level of common prosperity and the intensity of carbon emissions in China. Then the SDM model is applied to explore the effects of the common prosperity level on the intensity of carbon emissions. The findings reveal that: (i) The common prosperity level in China has shown an increasing tendency. Between 2006 and 2020, the mean level of common prosperity increased from 0.254 to 0.486. From the regional perspective, eastern China has seen greater levels of common prosperity than central China, while central China has experienced greater levels of common prosperity than western China; regional disparities in the degree of common prosperity are substantial among Chinese provinces from 2006 to 2020; the common prosperity level is relatively high in economically developed provinces and relatively low in economically backward provinces. (ii) China's carbon emission intensity shows a continuous downward tendency. The annual average intensity of China's carbon emissions decreased from 4.458 in 2006 to 2.234 in 2020. From the regional perspective, the three main regions' carbon emission intensity likewise exhibits a decline in tendency between 2006 and 2020; still, western China continues to have the greatest carbon emission intensity, following central China, while eastern China has the smallest; however, certain provinces, notably Inner Mongolia and Shanxi, continue to have high carbon emission intensity. (iii) China's common prosperity level and carbon emission intensity both exhibit positive spatial autocorrelation at a 1% significant level under the adjacency matrix. The spatial agglomeration effect is significant, and adjacent provinces can affect each other. (iv) The SDM (Spatial Durbin Model) model test with fixed effects finds that the increase in the level of common prosperity suppresses the intensity of carbon emissions in the local area and neighboring regions. (v) The mediating effects model indicates that the process of common prosperity suppresses carbon emission intensity through high-quality economic development, narrowing the income disparity, and the development of a sharing economy.

Does electricity price matter for innovation in renewable energy technologies in China?

The present study investigates the effect of public‐private partnership investment in energy and technological innovation on consumption‐based carbon emissions for China from 1990Q1‐2017Q2. This study employs generalized least square (GLS) based unit root test, Maki cointegration test, fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), canonical cointegration regression (CCR) and frequency domain causality test. The finding of this study shows that (a) the cointegrating relationship among public‐private partnership investment in energy, technological innovation, renewable energy consumption, exports, imports, and consumption‐based carbon emissions is proved; (b) exports, renewable energy consumption and technological innovation are helpful to lower consumption‐based carbon emissions; (c) both public‐private partnership investment in energy, GDP and imports leads to increased consumption‐based carbon emissions; (d) in the long term, public‐private partnership investment and technological innovation cause consumption‐based carbon emissions in China. This study recommends technological innovation for the cleaner production process and public‐private partnership investment in renewable energy.

Research on the dynamic relationship between China's renewable energy consumption and carbon emissions based on ARDL model

As the world's largest developing country, China is facing the serious challenge of reducing carbon emissions. The objective of this study is to investigate how China's aging population affects carbon emissions from the production and consumption sides based on an improved Kaya model. The advantage of the Kaya model is that it links economic development to carbon dioxide generated by human activities, which makes it possible to effectively analyze carbon emissions in relation to the structure of energy consumption and human activities. Based on different energy consumption structures and technological innovation levels, a threshold effect model is constructed. The results show that: (1) There is an inverted U-shaped curve relationship between population aging and carbon emissions in China. (2) Energy consumption structure and technological innovation thresholds can be derived for the impact of population aging on carbon emissions, with thresholds of 3.275 and 8.904 identified, respectively. (3) Population aging can reduce carbon emissions when the energy consumption structure does not exceed the threshold value. (4) There is no significant intervention effect of technological innovation on the relationship between population aging and carbon emissions. Based on the research results, some countermeasures and suggestions to reduce carbon emissions are proposed.

With a rising trend in global CO2 emissions due to industrialization, the role of renewable energy, technological innovation and green investment in curbing is critical. The main contribution of the current paper is to examine the impact of green investment, renewable energy consumption and technological innovation on CO2 emissions of 30 sample provinces of China from 1995–2019. The results of CS‐ARDL approach shows that renewable energy, technological innovation and green investment is important in abating CO2 emissions in China. Also, EKC for provincial data of China is confirmed. However, financial development escalates carbon emissions in China. It is also found that any policy change in green investment, financial development, renewable energy, technological innovation, and natural resource rent has strong implications for environmental quality of China. Therefore, shifting the economic structure to renewable energy is an important strategy to reduce carbon emissions. It is suggested that an adequate level of green investment projects should be initiated. Appropriate regulatory and economic drivers can boost investment businesses with more resources provided for the promotion of technical assistance.

Changes in crop mix significantly affect the carbon emissions from cropping systems, thus underscoring their importance in achieving sustainable agriculture development. This article aims to offer new insights into the relationship between crop mix changes and carbon emissions from cropping systems. Based on provincial statistical data on crop production, this study analyzed the spatiotemporal dynamics of crop mix and the subsequent effect on the carbon emissions from cropping systems in China from 2005 to 2020. The crop-mix type identification rules, emission-factor approach, and grey incidence analysis were applied. The results showed clear changes in crop mix and obvious transitions from grain crops to cash crops in southern provinces. Among crops, the abundance of maize, vegetables, and melons increased remarkably. Besides, the carbon emission intensity (CI) from cropping systems generally showed an upward trend in most provinces, with a spatial pattern of gradual decrease from southeast to northwest. The changes in CI were most closely correlated with rice, maize, vegetables, and melon, with coefficients of 0.808, 0.773, 0.814, and 0.792, respectively. Additionally, the growth of maize and vegetables ratios played a significant role in the CI increases in most provinces. To optimize crop cultivation and mitigate carbon emissions, possible strategies including rational crop production layout, land transfer, and clean production technologies were proposed.

Concerns about climate change, emission reduction, and environmental sustainability have become crucial in accomplishing long-term development goals. The present study explored the dynamic effects of financial development, renewable energy utilization, technological innovation, economic growth, and urbanization on carbon dioxide (CO2) emissions in India. This investigation quantifies short- and long-run dynamics using time series data from 1990 to 2020 and an Autoregressive Distributed Lag (ARDL) model. The outcomes from ARDL short- and long-run analysis revealed a positive and significant effect of financial development, economic growth, and urbanization on CO2 emissions in India. In contrast, both the short- and long-term coefficients for renewable energy utilization and technological innovation are negative and statistically significant, suggesting that expanding these variables will lead to lower CO2 emissions. The findings were validated by employing the Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegration Regression (CCR) methods. This research provides novel findings that add to the current literature and may be of special relevance to policymakers in the country because of the role that the financial system plays in environmental concerns.

China is vigorously building a unified domestic market, with priority given to regional market integration while maintaining a national unified market. Limited research has been conducted on whether market integration affects renewable energy technology innovation (RETI). This paper empirically studies this topic based on panel data of 30 Chinese provinces from 2004 to 2020 using FMOLS (Fully Modified Ordinary Least Squares), DOLS (Dynamic Least Squares method), and FGLS (Feasible Generalized Least Squares). Research results have been verified by robustness tests. The main conclusions are as follows: (1) Market integration has an important positive impact on RETI, namely, boosting it. This conclusion remains robust when different indicators of innovation and market segmentation are included. (2) The regional impacts of market integration on RETI are heterogeneous, being the greatest in east China, followed by west China and central China. (3) Market integration affects RETI through energy structure and technological innovation. It can optimize energy structure, improve technological innovation, and thus enhance RETI. Based on the above conclusions, in order to improve RETI in China and expand its contribution to carbon neutrality and carbon peaking, China needs to strengthen the construction of a national unified market and implement differentiated market integration policies in east, central, and west China. Furthermore, it is necessary to give full play to the role of energy structure and technological innovation in market integration by optimizing energy structure and improving technological innovation.

Does renewable energy technological innovation matter for green total factor productivity? Empirical evidence from Chinese provinces

Economic policy and energy policy are two major factors of energy consumption and carbon emissions in China. This paper analyzed China’s carbon emission intensity from two perspectives: per capita carbon emission of primary energy (CEPE) and final carbon emission intensity (FCEI), that is, final carbon emission per unit of GDP. Based on the latest available China statistics data, Divisia decomposition was applied to decompose the changes of carbon emission intensity. Study results showed that economic policy and energy policy factors in different periods impact on carbon intensity change and contribution rates are different. In terms of per capita CEPE, the level of economic development is major factors of per capita CEPE increase, which is increased by 309% and improved by 4.7 tCO2 during 1980–2012, where economic development promoted to increase per capita CEPE with contribution rate of 215.7%, while energy efficiency and structural changes played a role in reducing carbon emissions per capita. For analysis of FCEI which reduced by 52% during 1996–2012, energy efficiency is the main determinant of reduced emission intensity with the contribution rate of 106%, followed by the structural change of energy, while changes of industrial structure promote carbon emission intensity improvements. Thus, economic policy, energy policy in the different impacts on carbon emissions, and the contribution rate are different, and finally, preliminary recommendation and policy reflection for energy development policy were concluded.

Given the escalating challenges posed by global climate change, as the world's largest carbon emitter, China is facing a huge challenge in achieving its "dual carbon" goals. Therefore, reasonable prediction of China's carbon emission intensity is crucial for formulating effective emission reduction strategies. Considering the external shocks faced by the economic system, the time breakpoint is introduced into the traditional grey prediction model. The model is optimized from two aspects： accumulation method and background value, and a new grey breakpoint model with inverse accumulation is constructed. Based on the calculation of China's carbon emissions, the carbon emission intensity from 2023 to 2030 was predicted. The following conclusions were drawn： ① By adding time breakpoints, the new model achieved accurate prediction of the future trend of the system under external shocks, further reflecting the principle of information priority in the modeling process. ② Under the external impact of the COVID-19, the growth rate of China's GDP further slowed down, and the carbon emissions showed different characteristics in the four regions. The carbon emissions in the northeast began to decline gradually, while the carbon emissions in the eastern and western regions accelerated. ③ From 2023 to 2030, China's carbon emission intensity will considerably decrease. Compared with that in 2020, the carbon emission intensity is expected to decrease by 13.2% in 2025 and by 22.6% in 2030, with the highest decline in the northeast and the lowest in the east. However, under current conditions, China still finds it difficult to fully achieve its 2025 and 2030 emission reduction targets, with the eastern and western regions facing enormous pressure to reduce carbon emissions.