Carbon Intensity In China Research Articles

Research and development efficiency, state-owned enterprises, and carbon intensity in China

This study investigates the effects and transmission mechanisms of research and development (R & D) efficiency and the proportion of state-owned enterprises (SOEs) on carbon intensity using data from 30 Chinese provinces. The findings show that improving R & D efficiency can lower carbon intensity by promoting technological progress. However, more SOEs can increase carbon intensity by inhibiting technological progress. Thus, a higher proportion of SOEs can reduce the positive impact of R & D efficiency on decreasing carbon intensity. Additionally, the proportion of SOEs has a moderating effect that extends beyond provincial boundaries, resulting in spatial spillover due to strong interconnections between provinces. Heterogeneity analysis indicates that this moderating effect is particularly pronounced in the central and western regions, as well as in the electricity sector. This variation is due to differences in economic development levels and government priorities. Given the characteristics of China’s carbon intensity, policymakers should shift from a one size fits all carbon reduction policy to prioritizing enhancing R & D efficiency, boosting the innovation capabilities of SOEs, and considering spatial linkages and regional disparities.

Spatiotemporal Interaction Characteristics and Transition Mechanism of Carbon Intensity in China's Transportation Industry

This research was conducted using many spatial analysis approaches to dissect the spatiotemporal interactive characteristics of carbon emission intensity within the transportation sector from 2002 to 2020. An in-depth exploration of their transition mechanisms was conducted by nesting the obtained timewarp types with the panel quantile model. Finally, the geodetector model aligned with different transition mechanisms was employed to investigate and analyze the interaction effects among various factors influencing carbon intensity in the transportation sector. The results indicated that:① The carbon emission intensity of the transportation sector in 30 provinces and regions of China showed an overall downward trend with fluctuations, and the spatial clustering level was relatively stable. ② The spatiotemporal interactive features of ESTDA revealed that the relationship between the northwest region and its adjacent spatial units was unstable, with significant variations and fluctuations. In contrast, economically developed areas such as coastal cities in the eastern part had established mature transportation networks, resulting in a relatively stable local spatial pattern, though a few areas still exhibited spatiotemporal competitiveness. ③ The spatiotemporal transition of carbon intensity in the transportation sector could be categorized into four driving or constraining modes(the population economy urbanization constraint model, population economy urbanization facility constraint model, technology consumption industry-driven model, and technology industry regulation-driven model). Most provinces were influenced by the low quantile constraint and high quantile drive modes, with only a few affected by the high quantile constraint and low quantile drive modes, the majority of which were located in the northwest or southwest regions. ④ Further, we introduced the geographical detector model based on the identified mechanism of carbon emission intensity transition in the transportation sector, emphasizing the coordinated development of multiple factors and strengthening inter-regional collaborative governance.

Different types of environmental regulations and carbon intensity: empirical analysis of China’s garment industry

Different types of environmental regulations and carbon intensity: empirical analysis of China’s garment industry

Impact of Environmental Protection Tax on carbon intensity in China.

In the context of increasingly severe global climate change, finding effective carbon emission reduction strategies has become key to mitigating climate change. Environmental Protection Tax (EPT), as a widely recognized method, effectively promotes climate change mitigation by encouraging emission reduction behaviors and promoting the application of clean technologies. Based on data from 282 cities in China, this paper takes the official implementation of the EPT in 2018 as the policy impact and the cities with increased tax rates for air taxable pollutants as the treatment group and uses DID model to systematically demonstrate the relationship between the implementation of the EPT and carbon intensity (CI) and further explores the possible pollutant emissions and green innovation mediating effects. The findings show that (1) the implementation of EPT can effectively reduce CI by about 4.75%, and this conclusion still holds after considering the robustness of variable selection bias, elimination of other normal effects, policy setting time bias, and self-selection bias. (2) The implementation of EPT can reduce CI by reducing pollutant emissions and improving the level of green innovation. (3) There is obvious regional heterogeneity in the carbon reduction effect of EPT, and the implementation of EPT has a more significant effect on CI in medium-tax areas, low environmental concern areas, general cities, and eastern regions. This paper not only provides a new analytical perspective for systematically understanding the carbon emission reduction effect of EPT but also provides policy insights for promoting regional green transformation and advancing carbon peak carbon neutralization.

Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels

Excessive carbon emissions will cause the greenhouse effect and global warming, which is not conducive to environmental protection and sustainable development. In order to realize the goal of “carbon peak and carbon neutrality” as soon as possible, this paper utilizes the methodology provided by the IPCC to measure the carbon emissions and carbon intensity of China’s energy consumption. The classification method of carbon emission and the kernel density function method are used to explore the spatial and temporal evolution of regional carbon emissions. Based on the Log Mean Divided Index (LMDI) method, the drivers of China’s energy carbon emissions are measured. Based on the Tapio index function and the catch-up decoupling model, the decoupling status of Chinese provinces and the development gap with the benchmark provinces are examined. The results show that (1) China’s total energy carbon emissions show a “rising-declining-rising” trend from 2005 to 2021, and reach the first peak in 2013, totaling 1,484,984.406 million metric tons. China’s Hebei, Shanxi, and Shandong provinces have the highest energy carbon emissions. (2) China’s energy carbon emissions are influenced by multiple factors, and the contribution of each factor to energy carbon emissions is in the following order: economic development effect > energy intensity effect > energy structure effect > population size effect. (3) China’s catch-up provinces develop their economies at the expense of the environment and energy consumption.

Tracing carbon emissions and intensity in relational global value chain activities

Existing studies on emission accounting in global value chains (GVCs) have not focused on the relational nature of GVC activities manifested as the interaction of intermediate goods between enterprises. This paper proposes a relational GVC emissions accounting framework based on an inter-country input-output (ICIO) model distinguishing between domestic-owned enterprises (DOEs) and foreign-invested enterprises (FIEs), which is classified as trade-based, investment-based and hybrid-based emissions. Using AMNE-ICIO tables from 2005 to 2016, this paper traces emissions and carbon intensity in relational GVC activities at the global, economic, and industry levels. We find the following: (1) Investment-based emissions accounted for about 94.15% of the increase in relational GVC emissions, of which more than half comes from the interactive activities between DOEs and FIEs. (2) For the United States, high-income economies, and high-tech manufacturing, investment-based emissions were higher than trade-based emissions, and the reverse was true for upper-middle and lower-middle income economies and China. (3) Although relational GVC carbon intensity was high, the trend of decline was significant. (4) Compared to trade activities, investment-based activities were less carbon intensity in China and upper-middle income economies due to the interaction of production between FIEs and DOEs. The findings add new insights into the coordination of global emissions reductions and the allocation of emissions responsibility.

The impacts of casual environmental regulation on carbon intensity in China: Dual mediating pathways of energy low-carbon reconstitution and industrial structure upgrading

In the new era of global net zero, how to reduce carbon intensity became a hot topic, while few attentions had been paid to the impacts of casual environmental regulation, especially in the emerging economy such as China. In this regard, this study uses the panel data of 30 provinces in China from 2003 to 2019 to study the impacts of causal environmental regulation on the carbon intensity, with the adoption of multiple econometric models. The empirical results show that causal environmental regulation has a significant inhibitory effect on carbon intensity, while at current stage, causal environmental regulation cannot inhibit carbon intensity through the mediating of green innovation and renewable energy production. Fortunately, energy low-carbon reconstitution and industrial structure upgrading are proved to effectively act as the intermediary channels. In addition, the spatial spillover effects and the spatial heterogeneity are supported in the empirical analysis. In a summary, this study starts with the case of China to provide a realistic reference case for the international community, enriches the theory, policy and practice related to environmental regulation, and provides a realistic reference example for emerging economics in a developing country.

Energy regulation, energy innovation, and carbon intensity nexus in China: A nonlinear perspective

Considering the role of energy regulations and innovations in reducing energy-driven emissions, our main objective is to scrutinize the influence of energy regulations and innovations on carbon intensity in China over the period 1991–2020. In contrast to the previous studies, the analysis also focuses on the asymmetry assumption. The scientific value added to this study lies in its innovative methods and empirical evidence. The autoregressive distributed lag model, both linear and nonlinear, is utilized in the research to examine the short- and long-run estimates. The linear model highlights that energy regulations, energy innovations, and human capital reduce carbon intensity in the long run, while per capita income and financial development escalate it. However, only energy regulations and human capital help reduce carbon intensity in the short term. In the nonlinear models, the positive shock in environmental regulations reduces carbon intensity in short and long run. Conversely, the positive shock in energy innovations reduces carbon intensity in the long run. Negative shocks in energy regulations and innovations have a statistically insignificant impact on carbon intensity in both the short and long term. In addition, per capita income and financial development only increase carbon intensity in the long run, while human capital reduces carbon intensity in the short run. These findings advocate for implementing heavy energy taxes on firms and industries that rely heavily on fossil fuels. Increasing investment in green energy innovations is the need of the hour in lowering carbon intensity in China.

How China's digital technology development affects the environmental costs related to global value chains? Evidence from regional manufacturing sectors

China needs to address the environmental costs to participate in global value chains (GVCs) more sustainably. Digitalization presents a potential solution to overcome information barriers and improve energy efficiency in global production networks. However, the impact of digital technology on GVC-related carbon intensity has not been systematically studied. This paper utilizes embedded multi-regional input-output (EMRIO) tables to analyze the relationship between digital technology and GVC-related carbon intensity in China's 28 regions and 11 manufacturing sectors. The study calculates three indexes: digital technology development, the openness degree of digital technology, and the carbon intensity related to GVCs for the years 2002, 2007, 2010, and 2012. Through a fixed-effect model, the paper examines how digital technology can reduce domestic embodied carbon intensity related to GVCs in China. The findings indicate that: (1) digital technology development has a negative impact on GVC-related domestic embodied carbon intensity; (2) digital technology development indirectly reduces domestic embodied carbon intensity by decreasing energy consumption intensity and increasing foreign services' input intensity; (3) enhancing the openness of digital technology in China can strengthen the reduction effect of digital technology development on the environmental cost related to GVCs.

Can agricultural credit subsidies affect county-level carbon intensity in China?

Relieving credit restrictions can change the high-pollution producing style for agriculture, which is critical for low-carbon development. Using the time-varying difference in difference (DID) method and county-level panel data from 2000 to 2018 in China, we find that the agricultural credit subsidies (ACS) policy significantly reduces carbon intensity in the treated counties. For this effect, agricultural scale expansion and technology adoption are potential mechanisms. We further find that the effects are more salient in the areas with more primary industry outputs and high carbon emissions. Besides, other approaches show that our conclusions are robust. Subsidies for the banks' agricultural affairs direct credit into the agriculture sector and thereby promote carbon intensity reduction.

Effect of environmental taxes on environmental innovation and carbon intensity in China: an empirical investigation

Environmental taxes are deemed to be an important carbon mitigating factor and also encourage the producers to adopt environmentally friendly technologies. Therefore, the main objective of the analysis is to evaluate the environmental taxes on environmental innovations and carbon intensity in China. The novel quantile autoregressive distributed lag (QARDL) is applied to investigate the model empirically. From the results, we confer that the short and long run estimated coefficients of environmental taxes are positive and significant at most quantiles in the environmental innovation model, implying that the higher the rate of environmental taxes, the higher the rate of environment-related innovations. However, the estimates of environmental taxes are significant and negative in both short and long run at more than half quantiles in the carbon emissions model, suggesting the positive role of environmental taxes in reducing carbon emissions. In general, our findings imply that an increase in environment-related taxes helps reduce carbon intensity and promote environmental innovation in China. The study suggests that policymakers in China should promote environmental innovation and expand the environmental tax base to achieve the objective of carbon neutrality.

Effect of agricultural fiscal expenditures on agricultural carbon intensity in China.

Few studies provide direct evidences that agricultural fiscal affectsagricultural carbon intensity.This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders' birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions' carbon intensities are increased due to fiscal rivalry.

Environmental stewardship: Analyzing the dynamic impact of renewable energy, foreign remittances, and globalization index on China's CO2 emissions

Greenhouse gas emissions are a major hazard to the planet's ecosystem and humanity and contribute to global warming. Consequently, it's critical to investigate China's emission-inducing elements' core functions. To that end, the dynamic effects of renewable energy, foreign remittances, globalization, financial development, and economic growth on carbon intensity in China are explored in this research. The study analyzed data spanning from 1990 to 2020 and employed the linear Autoregressive Distributed Lag (ARDL) technique and various diagnostic tests. The findings of the linear ARDL methodology reveal that renewable energy mitigates the destruction of the environment; however foreign remittances, globalization, financial development, and economic growth exacerbate environmental deterioration in China. Furthermore, the outcomes specify that renewable energy has a negative short- and long-term impact on environmental erosion. Renewable energy sources are a tremendous tool for supporting China's initiatives to protect energy independence while reducing climate change. Additionally, concrete actions need to be taken by the Chinese government to minimize CO2 emissions to boost social prosperity and sustainability.

Towards carbon neutrality: the impact of innovative city pilot policy on corporate carbon intensity in China

ABSTRACT As part of an effort to explore and incentivize the development of innovation-driven, green and low-carbon cities, China implemented the innovative city pilot policy (ICPP) in 2008. Using the ICPP as a quasi-natural experiment, we investigate whether and through which channels the ICPP reduces corporate carbon intensity. To do this, we employ a staggered difference-in-differences (DID) econometric method and find that the ICPP indeed mitigates corporate carbon intensity. This relationship is shown to be quite robust using various alternative specifications of the DID analysis. The reduction effect on corporate carbon intensity is more pronounced for private firms, polluting firms, and capital-intensive firms, showing a convergence trend towards technology-intensive and clean firms. We find the ICPP reduces corporate intensity by enhancing corporate innovation and increasing the value-added rate (i.e. corporate upgrading). Based on these novel findings, we draw out policy implications for carbon neutrality, shedding light on pathways for addressing climate change and achieving sustainable development. Key policy insights Carbon neutrality is a long-term and ambitious target. Achieving this target requires the coordination and integration of multiple climate instruments. Among these, technological progress (e.g. renewable energy substitution and improved carbon efficiency) determines the overall cost of carbon neutrality. And innovation support policies should be included in the optimal climate policy package. Innovation policy can promote knowledge spillovers and long-term economic growth, as well as address environmental externalities of greenhouse gas (GHG) emissions, thereby providing a cost-effective way of combating climate change. Empirical evidence suggests that the ICPP implemented in China significantly reduces corporate carbon intensity and thus has promising prospects for the carbon-neutral target. The policy help firms enhance their innovation activities and facilitate corporate upgrading. As other emerging economies also face the sharp trade-off between climate change mitigation and economic growth, they can learn from this centrally-planned and locally-led innovation policy and lead their development mode onto a cleaner and decarbonized path.

How does industrial intelligence affect carbon intensity in China? Empirical analysis based on Chinese provincial panel data

With the deep integration of artificial intelligence and the real economy, the development of industrial intelligence brings important opportunities for green economic growth. However, few studies have provided insight into whether and how industrial intelligence affects carbon intensity. To bridge this gap, this study explores the impact of industrial intelligence on carbon intensity and how this impact fluctuates when industrial structure upgrading is included as a mediator, based on the spatial Durbin model (SDM) and the mediating effect model. The empirical results show that both industrial intelligence and carbon intensity have a significant spatial correlation. The development of industrial intelligence effectively reduces the carbon intensity of the local and neighboring regions with influence coefficients of −0.1031 and −0.1720, respectively. Considering the regional heterogeneity, the inhibitory effect of industrial intelligence on carbon intensity in the eastern region is strongest, followed by the central region, and negligible in western China. Industrial structure upgrading is an important mediating channel for industrial intelligence to function. Specifically, industrial intelligence can indirectly curb carbon intensity through promoting industrial structure supererogation and industrial structure rationalization, and the indirect effects account for 69.08% and 17.58% of the total effects, respectively. This study provides insights for policymakers to promote industrial intelligence in an effort to meet carbon reduction targets during economic transformation.

Contributors and drivers of Shanxi’s aggregate embodied carbon intensity (2002–2017) based on input–output and multiplicative structure decomposition analysis

Success stories of low carbon transition for energy enrichment areas in developing countries are scarce. Shanxi Province is a typical coal resource-based region with the highest carbon intensity in China. Determining the main contributors and drivers of high carbon intensities is a crucial issue for China to achieve carbon neutrality. This study constructed the environment extended input–output model and calculated Shanxi Province’s aggregate embodied carbon intensity (AECI) in 2002, 2007, 2012, and 2017. Then the contributors of AECI can be analyzed at the final demand level and sector level. A multiplicative structural decomposition analysis (SDA) was employed to investigate Shanxi’s AECI change drivers. The results show that outflow to other provinces replaced exports and became the primary contributor to AECI change after 2012. The four sectors, electricity, construction, mining, and metallurgy, contribute mostly to Shanxi’s AECI. The main driver of AECI change is gradually shifting from energy intensity to production structure and final demand factors. The framework and conclusion could be extended to the energy enrichment regions of developing countries to identify the critical action of low-carbon transition.

What are the roles of green technology innovation and ICT employment in lowering carbon intensity in China? A city-level analysis of the spatial effects

China is attempting to establish a feasible pattern for boosting economic structural transition, transforming energy structure, improving energy efficiency, and assuming responsibilities in the international community synchronously. This paper aims to investigate the nexus between green technology innovation (GTI), Information and Communication Technology (ICT) employment, and low carbon development. Drawing on the spatial Durbin model and panel data at the city level, I explored the impacts of GTI and ICT in lowering carbon intensity while considering spatial dependence from 2003 to 2017 in China. My empirical findings indicated that GTI and ICT not only play important roles in lowering local carbon intensity but also imposing positive and negative spatial spillover effects from the local cities to their neighboring cities. My examination of the transmission mechanism illustrates this concept except for the direct carbon mitigation impact. As a general-purpose technology, ICT also exerts an indirect effect in lowering carbon intensity via GTI. Heterogeneity tests reveal that both GTI and ICT have greater impacts on Eastern than Middle-Western regions to lower carbon intensity. Thus, policymakers should enhance the multi-dimensional region collaboration, the diffusion strength of informatization, and the absorptive capacity of technological innovation. The empirical evidence of this study also inspires confidence in accelerating industrial transition and optimizing energy structure toward carbon neutrality by boosting the expansion of ICT-related human capital.

The asymmetric impact of the new normal on China's carbon intensity: Reducing government investment carbon intensity but not citizen consumption carbon intensity

The new normal is a revolutionary change in China's economic development model. This work is aimed to explore the impact of the new normal on carbon intensity in China from the new perspective of carbon emission intensity of government investment and the carbon emission intensity of citizen consumption. To this end, a new two-layer structural decomposition model was developed based on multi-regional input-output model and structural decomposition analysis. The results show China's carbon intensity of government investment decreased by 27.4%, while the carbon intensity of citizen consumption remained barely changed after entering the new normal phase. This means the new normal has a significant effect on carbon intensity of government investment, but not on carbon intensity of citizen consumption. This finding corresponds to changes in the impact of investment structure on total carbon intensity before and after the new normal. Before the new normal, total carbon intensity was driven up by investment structure. The investment structure drove a 2.5% increase in carbon intensity between 2012 and 2015. However, after the new normal, the total carbon emission intensity was offset by investment structure. Investment structure affects carbon intensity change by −5.5% from 2015 to 2017. In addition, the decline in carbon emissions is uneven across regions. This is, carbon intensity of Inner Mongolia, Yunnan and Xinjiang have not decreased but increased, although the carbon intensity of most provinces has decreased after the new normal.

The green paradox puzzle: fiscal decentralisation, environmental regulation, and agricultural carbon intensity in China.

China has the highest carbon dioxide emissions worldwide. Exploring the mechanism of fiscal decentralisation on agricultural carbon intensity can help China reach its carbon peak and become carbon neutral. This study used panel data for 30 provinces in Mainland China from 2000 to 2019 and constructed a spatial Durbin model to investigate the spatial-temporal patterns and internal relationships among fiscal decentralisation, environmental regulation, and agricultural carbon intensity. The results indicated that (1) from 2000 to 2019, the agricultural carbon intensity showed a downward trend and showed a spatial pattern of 'high in the north and low in the south'. The degree of fiscal decentralisation has gradually increased, and the spatial pattern of 'high in the north and low in the south' has also emerged. The intensity of environmental regulation continues to increase, and the intensity of environmental regulation in inland areas is higher than that in coastal areas. (2) From 2000 to 2019, the global Moran index of agricultural carbon intensity showed a development trend of first rising and then falling, and the spatial correlation changed from strong to weak. Agricultural carbon intensity tends to develop from polarisation to balanced development. (3) Both fiscal decentralisation and environmental regulation can reduce agricultural carbon intensity, and environmental regulation has a negative spatial spillover effect. (4) Under the influence of fiscal decentralisation, environmental regulation is not conducive to reducing agricultural carbon intensity due to the characteristics of 'race to the bottom', causing the 'green paradox' effect. (5) Environmental regulation and fiscal decentralisation in main grain producing areas have less impact on agricultural carbon intensity than in non-main grain producing areas. Therefore, the central government should focus on optimising the fiscal decentralisation system, formulating a differentiated agricultural carbon emission control system, regulating competition among local governments, and optimising a political performance evaluation system.

The analysis of spatial-temporal effects of relevant factors on carbon intensity in China.

The increasing carbon emissions have been a major concern for most countries around the world. And as a result, every country is concerned about developing appropriate strategies to curtail it. As a major economy and the largest carbon emitter in the world, China has pledged to reduce the carbon intensity by 60–65% by 2030, compared with 2005 levels, and achieve carbon neutrality before 2060. Therefore, the analysis of the impact of China’s carbon intensity is becoming an increasing important topic. Due to the spatial heterogeneity of carbon intensity, various spatial econometric models have been applied in this field. However, the existing literatures failed to consider the cross-products of relevant factors. This paper constructs our dynamic general nesting spatial panel model (GNS) with common factors to deal with the dilemma, and examines the direct and spatial–temporal spillover effects of industrial structure, GDP per capita, investment in anti-pollution projects as percentage of GDP and energy price on carbon intensity in China over the period 2003–2017. Our analysis shows that: (1) China’s carbon intensity showed the spatial agglomeration and temporal “inertia” from 2003 to 2017. (2) From the time dimension, the long-term effect of industrial structure first increased and then gradually decreased. (3) From the spatial dimension, industrial structure and investment in anti-pollution projects as percentage of GDP accounted for the main spatial heterogeneity. Furthermore, this paper attempts to provide policy implications to help reduce carbon intensity and achieve carbon neutrality in China.