Decline In Carbon Intensity Research Articles

Drivers of carbon intensity decline during the new economic normal: A multilevel decomposition of the Guangdong case

The New Economic Normal is a revolutionary change in China's economic development model, and it is of great significance to understand the characteristics and drivers of carbon intensity (CI) under the New Economic Normal at multiple levels for formulating carbon mitigation plans. By coupling subsystem analysis and structural decomposition analysis, this study examines the characteristics and drivers of Guangdong's CI from 2007 to 2017 from multiple demand-side perspectives. It reveals in detail the impact of economic structural changes on CI during the New Economic Normal, especially demand transformations. Moreover, it outlines a methodology for providing further insights into the mechanisms of drivers in reducing CI from an industrial linkage perspective. The results show that 1) Interprovincial trade has replaced investment as the demand type with the highest CI during the New Norm. 2) Production restructuring has replaced energy efficiency improvement as the largest driver of CI decline. In contrast, changes in consumption patterns and demand conversion within the economic inner-cycle strategy impeded CI decline. 3) Production or demand restructuring has differential impacts on CI of different sectors or demand types, with offsetting effects even sometimes leading to insignificant overall impacts. 4) In industrial linkage, restructuring promoted CI associated with internal linkages and suppressed CI associated with cross-sectoral linkages, further altering the linkage characteristics of CI. The findings and research methodology will provide theoretical support for the formulation of carbon mitigation strategies in Guangdong and other regions.

Have consumption-based CO2 emissions in developed countries peaked?

This study innovatively divided consumption-based CO2 emissions of developed countries into domestic and foreign components using an environmentally extended multi-regional input–output model, and revealed their different driving factors using the structural decomposition analysis method. The results showed that the consumption-based emission peaked in 16 developed countries, with 14 countries peaking around 2008. Domestic emissions in all 16 developed countries had peaked, most of which accounted for 40–70% of the total consumption-based emissions. However, the foreign emissions driven by 9 peaking countries still kept increasing. Regarding domestic emissions, the decline of domestic carbon intensity was the main driving factor across 16 peaking countries. In terms of foreign emissions, carbon intensity decline, especially in main medium- and low-income countries, was the dominant factor in the CO2 emissions decrease. Significant improvements in production technology levels of medium-income countries played a key role in weakening the carbon-increasing effects of foreign emissions during the post-peak period. Thus, to further promote global carbon emissions to peak as soon as possible, peaking developed countries should provide more emission reduction funds and technologies to support the decline in carbon intensity and the improvement of production technology in medium- and low-income countries.

TACKLING CARBON INTENSITY WITH GREEN FINANCE IN THE COVID-19-ERA: RECOMMENDATIONS FOR OECD ECONOMIES

Green financing has been examined in the literature. However, its impact on carbon intensity has not been fully investigated. This research sets out to fill this gap by using the dimensions of green loans, securities, insurance, and investment. In exploring the connections between green financing, nonfossil energy use, and carbon intensity, we utilized data from 2016 to 2020 to run an advanced quantile modeling. We applied the decision-making unit-method of data envelopment analysis for analyses. Our main findings are as follows. Rapid advances in the green finance sector in Organisation for Economic Co-operation and Development countries were coupled with an increase in nonfossil energy usage, resulting in a decline in carbon intensity. When the growth in nonfossil energy consumption was reduced, green investment was put on hold, and the green financing industry would be negatively impacted. The role of green financing and carbon intensity in nonfossil energy use is coupled with strong government policy interventions. Nonetheless, the effects of green finance initiatives often lag. Moreover, these effects are inconsistent. This research suggests new methods to increase the use of nonfossil energy, build a carbon trading market, and increase the consumption of green financing policies post COVID-19.

Spatial spillover effect of carbon emission trading on carbon emission reduction: Empirical data from pilot regions in China

To estimate the comprehensive impact of China's carbon trading policy launched in 2013 on carbon emissions, this paper conducts a quasi-natural experiment with the combination of difference-in-differences method (DID) and spatial DURBIN model (SDM), further analyzes the mechanism from the perspective of energy use, energy structure and energy efficiency. The empirical results show that, (1) The CET has reduced the carbon emissions in pilots since 2013 significantly. Carbon emissions in neighbouring regions of pilots are also reduced, which proves the existence of spatial spillover effects from CET. Compared with the direct part, the spatial spillover effect accounts for smaller proportion. Both the results of robustness and endogenous analysis support above conclusion. (2) The effect of CET on carbon emissions continues to increase over the long term, while the spatial spillover effect gradually decreases as distance increases. (3) The CET also contributes to the decline of carbon intensity. (4) The decline of fossil energy use is the main path to reduce carbon emissions, while the transformation of energy structure seriously hinders the CET to achieve carbon emission reduction in the long term.

Emission accounting and drivers in 2004 EU accession countries

The ten countries that joined the European Union (EU) in 2004 (Cyprus, Czechia, Estonia, Hungary, Lithuania, Latvia, Malta, Poland, Slovakia, and Slovenia) have experienced faster economic growth and slower declines in energy consumption than traditional EU members. As designing of low-carbon policies requires accurate CO2 emission accounting, this study describes the evolving trajectories of CO2 emissions from 2005 to 2017 of 2004 EU accession members by providing detailed emission inventories by 28 types of energy and 47 socioeconomic sectors. We further quantify the contributions of four socioeconomic drivers (i.e., economic growth, energy structure, carbon intensity, and energy intensity) to the emission changes. The results show that the total CO2 emissions of the ten countries decreased by 7.50% from 2010 (506.81 Mt) to 2016 (468.78 Mt), which is lower than the average decline rate of other EU members (10.52%). Although the effect of economic growth contributed the most to emission increase (15.44%), it is completely offset by the decline in carbon intensity (-18.82%). We also discuss potential roadmaps towards carbon neutrality by designing 33 scenarios based on the European Union Low-Carbon Development Map 2050. We find that carbon neutrality cannot be achieved unless the share of renewable energy sources reaches 60% and more than half of existing coal and gas power plants are upgraded to Carbon Capture Storage (CCS) technology. These changes require the implementation of both short-term and long-term strategies.

Analysis of Influencing Factors of Embodied Carbon in China’s Export Trade in the Background of “Carbon Peak” and “Carbon Neutrality”

Since China’s reform and opening up, especially after its accession to the World Trade Organization, its foreign trade has achieved fruitful results. However, at the same time, the extensive foreign trade growth model with high energy consumption and high pollution has also caused a rapid increase in carbon emissions. There is a large amount of embodied carbon emissions in the export trade. In order to achieve the strategic goals of “Carbon Peak” and “Carbon Neutrality’, and at the same time build a green trading system to achieve coordinated development of trade and the environment, it is of great significance to study embodied carbon emissions and how to decouple them with China’s foreign trade. This paper uses the Logarithmic Mean Divisia Index method to decompose the influencing factors of the embodied carbon in China’s export trade in order to study the impact of three factors: export scale, export structure, and carbon emission intensity. The results show that the change in export scale is the most important factor affecting the embodied carbon of China’s export trade, and the expansion of export scale has caused the growth of trade embodied carbon. Carbon emission intensity is the second influential factor, and the decline in carbon intensity would slow down the growth of trade embodied carbon, while changes in the export structure have the smallest impact on trade embodied carbon. The high carbonization of the overall export structure will cause growth of trade embodied carbon, but the tertiary industry has seen some improvement in the export structure, which could facilitate the decline of trade embodied carbon.

The Evolution of Factors Influencing Green Technological Progress in Terms of Carbon Reduction: A Spatial-Temporal Tactic Within Agriculture Industries of China

The impacts of widespread carbon emission trends possessed tremendous pressure for global food security, sustainable development, and ecosystems. Several temporal and spatial patterns of green technology have been adopted to reduce carbon emissions in different regions of China. In China, agriculture industries may have colossal importance for reducing carbon emissions. On the basis of the data from 1998 to 2018, the study uses the heterogeneous stochastic frontier model to quantify the carbon emission reduction potential of agricultural green technology progress in eastern, central, and western regions of China by using the heterogeneous stochastic frontier model. We also analyze the coefficient of variation and its spatial and temporal evolution pattern of carbon intensity decline potential index and explore the potential factors related to the agriculture green technology progress of China. The finding of the study revealed that the carbon emission rate in the agriculture industry of China is very high, whereas adopting green technology is slower because of economic and policy-related factors—the carbon emission of green technological progress. In terms of spatial variations, the changes in various regions were consistent with the overall fluctuating rate compared with the state of another country, but an increasing trend has been traced within the “east-central-west” regions. The overall regional differences are gradually trending, but differences between regions mainly cause them. The increase in the structure of the agricultural agriculture industry, the level of labor, and the increase in administrative environmental regulations will weaken the obstacles to the carbon emission reduction potential of green technological progress. The increase in urbanization, the level of the agricultural economy, and economic and environmental regulations will increase the carbon emission reduction potential of green technological progress. It is necessary to actively promote exchanges and cooperation in green agricultural technology and advanced management concepts, accelerate the optimization and upgrading of the industrial structure, and achieve the goal of peaking carbon emissions through regional coordinated development. Regionally, the overall external environment and the level of green technology progress in the western region need to be improved in all respects. The central and eastern regions need to focus on combining different policy tools to transform them from hindrance to promotion.

The decline in carbon intensity: the role of financial expansion and hydro-energy.

The effect of a broad financial index (FD), hydro-energy output, and expansion of the economy on the intensity of carbon were investigated by incorporating the effect of financial expansion through the channel of hydro-energy. The cross-sectional autoregressive distributed lag (CS-ARDL), Mean Group (MG), Augmented Mean Group (AMG), and vector error correction mechanism causality approaches were employed to study panel data for the period from 1980 to 2017 for the top four hydro-energy producing states. The outcomes revealed that FD raises the intensity of carbon in both the short and long term. In contrast, hydro-energy output (HYP) is stated to be a significant factor for attaining a low carbon intensity in the short and long term. The outcomes also indicated that the expansion of the economy augments the intensity of carbon. However, FD lowers the intensity of carbon via the channel of hydro-energy. The causality test outcomes revealed a short-run causality moving from the intensity of carbon to hydro-energy as well as a short-term causality moving from GDP to the intensity of carbon. It was also observed that there is a long-term causality running from all the variables to the intensity of carbon. Important policy implications are suggested at the end of the research.

Can energy quota trading reduce carbon intensity in China? A study using a DEA and decomposition approach

Energy quota trading (EQT) is a market-driven method to reduce distortions in energy allocation. In 2016, an EQT policy was proposed in China. However, the economic and environmental effects of this new policy remain unclear. To study these problems, this paper conducts an empirical analysis using data envelopment analysis (DEA) and index decomposition analysis (IDA). Following a simulation of the optimal energy input and maximum output in three sectors of the 30 Chinese provinces that have adopted China's EQT policy, the main conclusions are as follows: (1) The energy consumption of the industrial sectors is excessive, yet the primary and service sectors need further energy input, while there is vast economic potential in China's western provinces and the service sectors. (2) The optimal energy intensity effect generated by the trading policy is the largest impetus of carbon intensity reduction in China, but the obstacles to carbon intensity reduction are different in each region. (3) EQT has the potential to drive a significant decline in carbon intensity in China, resulting in a simulated average annual decline of approximately 4.09% from 2005 to 2016.

Solely economic mitigation strategy suggests upward revision of nationally determined contributions

Solely economic mitigation strategy suggests upward revision of nationally determined contributions

Spatial-temporal analysis of China's carbon intensity: a ST-IDA decomposition based on energy input-output table.

It is of crucial importance to identify the driving factors for emission changes since China's commitment to reduce carbon intensity in 2009. Hence, the spatial-temporal variation of carbon intensity of China's 30 provinces from 2010 to 2017 is explored by applying a Spatial-temporal Index decomposition analysis (ST-IDA) model combined with energy input-output analysis. Industrial structure, energy intensity, energy structure, and carbon emission coefficient are identified as driving factors; simultaneously, a new factor, energy conversion efficiency, is also introduced based on the energy input-output analysis, which is of significance as China is vigourously pushing electricitification. The results show that the carbon intensity of economic sectors in most provinces declined from 2010 to 2017. Energy intensity is the biggest contributor to both the temporal decline of carbon intensity and its spatial difference for economic sectors, followed by industrial structure, energy conversion efficiency, energy structure and carbon emission coefficient, while the rank of inhibition of each factor is the same as above. Meanwhile, the carbon intensity of the residential sector is mainly affected by per capita GDP and per capita energy consumption. Related policy suggestions are given.

Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement

The global economy is facing a serious recession due to COVID-19, with implications for CO2 emissions. Here, using a global adaptive multiregional input–output model and scenarios of lockdown and fiscal counter measures, we show that global emissions from economic sectors will decrease by 3.9 to 5.6% in 5 years (2020 to 2024) compared with a no-pandemic baseline scenario (business as usual for economic growth and carbon intensity decline). Global economic interdependency via supply chains means that blocking one country’s economic activities causes the emissions of other countries to decrease even without lockdown policies. Supply-chain effects contributed 90.1% of emissions decline from power production in 2020 but only 13.6% of transport sector reductions. Simulations of follow-up fiscal stimuli in 41 major countries increase global 5-yr emissions by −6.6 to 23.2 Gt (−4.7 to 16.4%), depending on the strength and structure of incentives. Therefore, smart policy is needed to turn pandemic-related emission declines into firm climate action. Global emissions could decrease 3.9–5.6% over 5 years due to COVID-19, and the interconnected economy means lockdown-related declines reach beyond borders. As countries look to stimulate their economies, how fiscal incentives are allocated and invested will determine longer-term emission changes.

What determines the climate mitigation process of China's regional industrial sector?

As a major carbon emitter in China, the emission mitigation in industrial sector performs great significance for China to achieve its emission reduction targets. Using the provincial panel data during 2000-2016 of China's industrial sector, this paper first used a gravity model to study the spatial distribution and center of gravity of industrial CO2 emissions. Then, an integrated decomposition approach based on Shephard distance functions was adopted to study the driving factors of industrial carbon intensity. Results indicate that during 2000-2016, industrial CO2 emissions center of gravity gradually moved to the west. China's industrial carbon intensity achieved considerable decline, with the annual change rate of 8.27%. The energy intensity decline, technology progresses of both production and energy saving were the most important factors facilitating carbon intensity decline. However, energy structure adjustment exerted positive effects in carbon intensity increase, although its effects were minor. Industrial carbon intensity witnessed decrease in almost all provinces except Xinjiang. The effects resulted from various factors were also different across provinces. Finally, suggestions were proposed to further decrease industrial carbon intensity.

Embodied carbon emissions in the supply chains of multinational enterprises

Enterprises are at the forefront of climate actions and multinational enterprises (MNEs) engage in foreign direct investment, allowing them substantial influence over the entire supply chain. Yet emissions embodied in the international supply chains of MNEs are poorly known. Here we trace the carbon footprints of foreign affiliates of MNEs and show that the gross volume of global carbon transfer through investment peaked in 2011, mainly driven by the decline in carbon intensity. Despite declining carbon footprints of developed country-based MNEs, there has been a notable increase in carbon transfer sourced from the Chinese mainland. We propose an investment-based accounting framework to allocate carbon footprints of MNEs to the investing country. Investment-based accounting of emissions could inform targeted and effective climate policies and actions. For instance, some large MNEs play a crucial role in carbon transfer, therefore their originating country should bear more responsibilities of carbon emission reduction as an investor. Multinational enterprises and their international supply chains can have large carbon footprints, but there is mitigation potential. Global carbon transfer through investment has declined in recent years, and this framework, assigning emissions to the investing country, would inform further action.

Net primary productivity‐based factors of China's carbon intensity: A regional perspective

AbstractReducing carbon intensity (CI) is one of the core steps of climate change mitigation. This study emphasized the roles of ecological services and regional heterogeneity in determining CI. We considered heterogeneities based on geography and income and explored the roles of net primary productivity (NPP)‐based carbon footprint and CI in the changes in China's CI over 2001−2015 using an extended production‐theoretical decomposition analysis and matching the socio‐economic data sets with NPP data from a remote sensing satellite. We found that group technological change, reflecting the effect of shrinkage or expansion of the group best practice frontier, and the potential NPP‐based carbon footprint, reflecting the impact of energy‐related CO2 emissions on ecological carbon absorption, are the most significant factors accounting for the increase and decrease in CI, respectively, while NPP‐based CI generally accounts for the decline in CI. We further showed that the technology gap change exhibited by an invert U‐shaped curve contributed to the increase in CI under geography‐based heterogeneity. We advise that China's policies should be more focused on ecological factors and regional heterogeneity in regions with abundant NPP (e.g., Yunnan and Sichuan) to further reduce CI.

Environmental and economic effects of China’s carbon market pilots: Empirical evidence based on a DID model

This paper investigates whether the China ETS policy has achieved carbon emission reduction at the expense of economic development. Moreover, we are interested in unmasking the role of the institutional factors adopted by the ETS pilots in their ETS effects. Using the province-level panel data during the 2008–2016 period. we employ the DID model to compare carbon emissions and economic development between the ETS and non-ETS regions and between the pre- and post-ETS periods. Some novel empirical findings emerge. First, compared with the non-ETS areas, the ETS policy has significantly reduced carbon emission in the ETS areas. This emission reduction has not come at the cost of economic development. Second, the ETS policy leads to a decline in carbon intensity and fossil fuel energy consumption relative to all energy types. Lastly, some heterogeneity across markets arise. The Beijing carbon market performs the best among all pilots in terms of achieving targets of carbon reductions, followed by the Hubei carbon market.

Does the development of renewable energy promote carbon reduction? Evidence from Chinese provinces

Developing renewable energy is a crucial measure in addressing climate change and achieving carbon reduction. However, research evidence on its impact is mixed. To fill this gap, we construct a panel quantile regression model in this study to examine whether China's renewable energy development has effectively promoted a reduction in carbon emissions using panel data of 30 Chinese provinces from 2005 to 2016. The results show that: (1) Improving China's renewable energy development level is conducive to carbon emission reductions. Specifically, carbon intensity could drop by 0.084%–0.149% for every 1% increase in renewable energy generation. However, the inhibitory effect is limited due to trapped electricity as well as the fact that substituting renewable energy for fossil energy has not yet sufficiently transformed the energy consumption structure. (2) Renewable energy development has a greater impact on carbon intensity reduction in regions with high or low carbon intensity than in areas with intermediate carbon intensity. (3) The main factor in the decline in carbon intensity in China is a decrease in energy intensity. Nonetheless, the role of renewable energy in carbon reduction has increased over time.

Why does China’s carbon intensity decline and India’s carbon intensity rise? a decomposition analysis on the sectors

As more attention paid to global warming, the international community argues that developing countries should take more carbon reduction responsibility. China and India, the top two developing countries confronted heavy pressure to handle the dilemma of “environmental protection or economic increase”. In this context, we switched focus to carbon intensity (defined as carbon emission per unit of economic output) to investigate carbon performance and economic performance simultaneously. In this study, we fully considered the impact of sector heterogeneity on carbon intensity, that is, we decomposed the carbon intensity of the 56 industries, which subsequently reclassified into 18 industries in China and India using the data from WIOD (world-input-output database). The results indicate that: (1) India’s carbon intensity presented a constant increasing trend, while China appeared a stable decreasing trend, and the former exceeded the latter since 2009; (2) all industries of China exerted efforts to decrease carbon intensity, conversely, India only had a few industries decreasing carbon intensity; (3) energy intensity significantly decreased China’s carbon intensity, while increased India’s carbon intensity surprisingly; (4) in general, structure effect made negative contribution to carbon intensity improvement in China, while positive in India. Specifically, economic structure had a positive impact on China and India, though the impact was minor for the former, significant for the latter; energy structure slightly promoted India’s carbon intensity to decrease, on the contrary, it was the largest inhibitor to China. This research will not only promote low-carbon economy development of China and India, but also provide scientific and practical references to other developing countries.

Spatial Heterogeneity of «Green» Economy and Transaction Costs in Forestry

In this paper authors provide the further elaboration in a quantitative spatial analysis of the Russian regions in the context of the green economy concept. Two quantitative factors characterizing the population welfare are considered: the total wage fund and the regional public financial fund from own revenues per capita. As key environmental indicators, the authors chose eco-intensity indicators for regional economic systems. Our estimates show substantial heterogeneity of the Russian sub-federal units on socio-environmental characteristics. It is shown that the population of many regions not only lives under high anthropogenic pressure (overall and per capita), but they are also not provided with additional resources “collective welfare” at the expense of own budget revenues, despite the disadvantageous environmental conditions. It was noted that “carbon regulation”, directed at development a low-carbon economy, should not be based on quantitative indicators, which is the same for the whole country. Due to the heterogeneity it will require fine-tuning, taking into account regional specificity of the relationships between factors of well-being and anthropogenic pressures. The aim to decline of carbon-intensity in local economies in different regions of the country will require different timelines, and the relevant “road maps” need to be taken into account. In public policy to reduce carbon emissions, special attention should be paid to the differentiation of transaction costs for managing, protecting and restoring forest ecosystems. In state policy on carbon reduction issues, one of the focuses of special attention should be on differentiating the transaction costs of managing, protecting and restoring forest ecosystems.

Decomposition of CO2 emission intensity in Chinese MIs through a development mode extended LMDI method combined with a production-theoretical approach

An improved understanding of the influence of development mode on carbon intensity (CI) can aid China’s manufacturing industries (MIs) to reduce CI without affecting the development of Chinese MIs. To demonstrate the relationship between the development mode and CI well, this work provides the following contributions: (a) The driving factors of Chinese MIs’ CI are decomposed by a development mode extended Logarithmic Mean Divisia Index method (LMDI)); (b) The impact of the factors of industrial development mode is analyzed by comprehensively by the extended LMDI combined with production -theoretical decomposition analysis (PDA). (c) The disparities of the driving factors in different industrial classifications are evaluated by applying the development mode extended LMDI combined with PDA in Chinese MIs between 2000 and 2015. Results showed that, (1) at the entire MIs’ level, the effects of ratio of energy consumption to R&D (RI) and potential carbon intensity (PCI) were the two leading contributors to the CI decline in Chinese MIs. (2) In terms of the cumulative effects at the subsectoral level, PCI had the largest curbing effect on the CI of all subsectors, and investment intensity (II) had the greatest stimulating effect on the CI of all subsectors. (3) Among the three industrial classifications, the middle-end MIs experienced the largest carbon intensity decline from the 10th Five-Year Plan (FYP) to the 12th FYP, followed by the low- and high-end MIs. RI and PCI decreased the CI of Chinese MIs in all industrial classifications and economic development stages. Potential energy efficiency and II were the two major contributors to CI improvement in all industrial classifications and economic development stages.