Carbon Emission Reduction Policies Research Articles

A daily carbon emission prediction model combining two-stage feature selection and optimized extreme learning machine.

Global warming caused by increased carbon emissions is a common challenge for all mankind. Facing the unprecedented pressure of carbon emission reduction, it is particularly important to grasp the dynamics of carbon emission in time and accurately. This paper proposes a novel daily carbon emission forecasting model. Firstly, the daily carbon emission data is decomposed into a series of completely noise-free mode functions by improved complete ensemble empirical mode decomposition method with adaptive noise (ICEEMDAN). Then, a two-stage feature selection method composed of partial autocorrelation function (PACF) and ReliefF is applied to select appropriate input variables for the next prediction process. Finally, the extreme learning machine optimized by improved sparrow search algorithm (ISSA-ELM) is used to predict. The empirical results show that the proposed two-stage feature selection method can further improve the prediction accuracy. After two-stage feature selection, the values of R2, MAPE, and RMSE were improved by 0.55%, 30.23%, and 28.46%, respectively. It can also be found that ISSA has good optimization performance. By combining with ISSA, R2, MAPE, and RMSE improved by 7.60%, 31.97%, and 44.79%, respectively. Therefore, the proposed model can provide a valuable reference for the formulation of carbon emission reduction policies and future carbon emission prediction research.

Carbon Emissions Estimation and Spatiotemporal Analysis of China at City Level Based on Multi-Dimensional Data and Machine Learning

Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China’s economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.

Does the digital economy contribute to carbon emissions reduction? A city-level spatial analysis in China

The rapid development of the digital economy provides an unprecedented opportunity for China to achieve carbon neutrality by 2060. While previous studies have explored the relationship between the digital economy, digital technologies, and energy, the impact of the digital economy on carbon emissions has not received sufficient attention in the literature. Meanwhile, although cities are the basic units for carbon emission reduction policies, few studies have explored carbon emissions at the city level in China. This study investigates the spatial correlation and spillover effects of the digital economy development on carbon emission reduction in 248 prefecture-level cities in China from 2011 to 2019. The proposed approach relies on Moran’s I test and the spatial Durbin model (SDM). First, the study’s results show that carbon emissions are more severe in industry-intensive cities in northeast and central China than in the eastern coast during the sample period. Second, the development of the digital economy is conducive to reducing carbon emissions. Third, Moran’s I and SDM tests find that the digital economy has a significant spatial effect on carbon emissions, with a close spatial connection between cities. Based on the above findings, this study offers relevant policy recommendations and suggestions for realizing China's carbon neutrality goal.

A Heterogeneity Study of Carbon Emissions Driving Factors in Beijing-Tianjin-Hebei Region, China, Based on PGTWR Model.

The Beijing–Tianjin–Hebei region is an important economic growth pole in China and achieving carbon emission reduction in the region is of great practical significance. Studying the heterogeneity of the influencing factors of carbon emission in this region contributes to formulating targeted regional carbon emission reduction policies. Therefore, this paper adopted thirteen cities as individuals of cross-section and conducted spatial and temporal heterogeneity analysis of the influencing factors of converted carbon emissions in the region with panel data from 2013 to 2018 based on the PGTWR model. From a space-time perspective, the regression coefficient of each influencing factor in this region has obvious heterogeneity, which is mainly reflected in the time dimension. In the study period, the impact of industrial structure, the level of urbanization, energy intensity, and the level of economic growth on carbon emission showed a decline curve, while the impact of the level of opening up and the size of population was on the rise, indicating that more attention should be paid to the latter two factors for the time to come. In terms of space, the differences in the influence of industrial structure and energy intensity on carbon emission vary significantly.

Hybrid dynamic coal blending method to address multiple environmental objectives under a carbon emissions allocation mechanism

The rise in global carbon emissions has led to serious ecological problems and highlighted the need to better pursue the sustainable development of coal-fired power plants. To reduce environmental pollution, this paper proposes a hybrid dynamic coal blending method under an uncertain environment to achieve a trade-off between economic development and environmental protection. This method fully considers seasonal pollutant variations, a dynamic carbon emissions quota allocation mechanism. A practical case from the Jintang coal-fired powered plant is given to verify the efficiency and practicality of the proposed optimization method. The model allows decision-makers to promote cleaner production by developing coal blending schemes to suit relevant national and local carbon and inhalable particulate matter emissions reduction policies. The results of the sensitivity analysis also confirm this view and show that the model is stable and has emission reduction limits. Specifically, compared with the maximum emissions, the model can reduce carbon emissions by about 29.1% (2.625 × 106 tonnes) and inhalable particulate matter emissions by 29.7% (0.64 × 106 tonnes). The results demonstrated that the hybrid dynamic coal blending method was able to achieve carbon emissions and inhalable particulate matter emissions reductions and that appropriate environmental constraints can improve emissions performance and encourage sustainable CPP development.

Risk assessment of cascading failure of distribution network with flexible multi-state switch

With the implementation of carbon emission reduction policies in various countries, the penetration rate of distributed generation (DG) has gradually increased. Flexible multi-state switch (FMS), has attracted more attention as a new power device. This paper analyzes the cascading failure risk of flexible interconnected distribution system caused by the hidden failure of FMS protection. Firstly, based on the physical structure of FMS, the transient characteristics of AC feeder failure and the control and protection strategy of FMS are analyzed. Then, the uncertain factors causing protection misoperation are put forward and modelled. On this basis, the mechanism of cascading failures formation is analyzed. The risk index of cascading failure in flexible interconnected distribution network and the optimal load shedding based on failure consequences are established. Finally, the risk of system cascading failure is evaluated by the improved IEEE 33-bus system. Also, the influence of fault location and DG penetration on the risk is analyzed.

Determinants and regional contributions of industrial CO2 emissions inequality: A consumption-based perspective

The assessment of industrial consumption-based carbon dioxide emissions could explain interregional differences in carbon dioxide emissions resulting from economic activity. This study aims to assess per capita industrial consumption-based carbon dioxide emissions inequality across provinces, municipalities, and autonomous regions in China from 2002 to 2017. The status and main determinants of per capita industrial consumption-based carbon dioxide emissions inequality are calculated using environmentally multi-regional input–output models and the Theil index, respectively. Comparisons with accounting results from the production side are then analyzed. The main conclusions are as follows: (1) From 2002 to 2012, both per capita industrial consumption-based carbon dioxide emissions and per capita industrial production-based carbon dioxide emissions in China show an obvious upward trend, and gradually stabilize in 2015 and beyond. (2) Economic trade leads to obvious carbon leakage among Chinese provinces and industrial sectors: the consumption-based carbon dioxide emissions of the downstream sector of the industrial supply chain is less than its production-based carbon dioxide emissions, while the carbon dioxide emissions of upstream industrial sectors is on the contrary. (3) China's per capita industrial production-based carbon dioxide emissions inequality is significantly greater than consumption-based inequality, and this gap is gradually widening. Among them, between-group inequality is the key component, but the contribution of within-group inequality is gradually increasing, which tends to exceed between-group inequality. (4) Among the eight economic groups in China, per capita consumption is the main driving factor of consumption-based carbon dioxide emissions inequality, but the industrial production carbon dioxide emissions intensity is the main contributor to production-based inequality. The above findings would provide certain theoretical support and policy references for policymakers when formulating industrial carbon emissions reduction policies.

Carbon emissions trading system and investment efficiency: Evidence from China

The economic consequences of environmental regulations remain controversial. This study examines the impact of carbon emissions trading on enterprises’ investment efficiency. The results show that carbon emissions trading restrains over-investment and promotes the investment efficiency of enterprises. Additionally, this relationship is more significant in enterprises in higher levels of corporate governance and state-owned enterprises. This study enriches the literature on emissions trading systems from the perspective of micro resource allocation efficiency, enriches the application of the Porter hypothesis, and expands the relevant research on the factors affecting enterprise investment efficiency. Moreover, the results have important practical significance for governments implementing carbon emission reduction policies and enterprises participating in carbon emissions trading.

A novel short-term carbon emission prediction model based on secondary decomposition method and long short-term memory network.

Grasping the dynamics of carbon emission in time plays a key role in formulating carbon emission reduction policies. In order to provide more accurate carbon emission prediction results for planners, a novel short-term carbon emission prediction model is proposed. In this paper, the secondary decomposition technology combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is used to process the original data, and the partial autocorrelation function (PACF) is applied to select the optimal model input. Then, the long short-term memory network (LSTM) is chosen for prediction. The secondary decomposition algorithm is innovatively introduced into the field of carbon emission prediction, and the empirical results illustrate that the secondary decomposition technology can further improve the prediction accuracy. Combined with the secondary decomposition, the R2, MAPE, and RMSE of the model are improved by 2.20%, 43.08%, and 36.92% on average. And the proposed model shows excellent prediction accuracy (R2 = 0.9983, MAPE = 0.0031, RMSE = 118.1610) compared with other 12 comparison models. Therefore, this model not only has potential value in the formulation of carbon emission reduction plans, but also provides a valuable reference for future carbon emission forecasting research.

The Impact of Economic Growth and Trade Openness on Environmental Degradation: Evidence from A Panel of ASEAN Countries

Global warming and climate change show a decrease in environmental quality. The main cause of global warming is the emission of carbon dioxide (CO2). This study aims to analyze the effect of economic growth and economic openness on environmental quality in ASEAN countries from 2010 to 2019. This research uses a panel data analysis method. The analysis results show that the variable economic growth has a negative and significant effect on carbon dioxide emissions. Meanwhile, the variables used for economic openness are exports, imports, and FDI. Export and FDI variables have a positive and significant effect on CO2 emissions, but there is no effect on imports. This study also used population variables as control variables and the result is there is no effect on CO2. Based on the results, it is necessary to have a carbon emission reduction policy for ASEAN countries using environmentally friendly technology.

Review of recent progress of emission trading policy in China

Global warming caused by the excessive combustion of fossil fuels has become the focus of the international community. China's carbon emission reduction policy plays a vital role in international climate change, carbon emission neutrality and domestic environmental pollution control. This paper summarises the progress of China's Emission Trading Scheme (ETS) pilots from 2011 to 2021, including trading volume, carbon emissions price, and the effect of carbon emission abatement. By calculating the carbon emissions intensity decrease rate of China's ETS pilot and non-pilot regions, the ETS and carbon emissions tax are compared in terms of emission reduction effectiveness, emission reduction cost and implementation resistance to provide theoretical support for the Chinese government to select and develop carbon emission reduction tools. The development of the Clean Development Mechanism in China is also compared with China Certified Emission Reduction (CCER), and the offsetting rules of CCER are summarised. After the whole analysis and assessment, the results show that from 2013 to 2018, the average carbon emissions intensity decline rate in the ETS pilots was 7.3% and that in non-pilots was 4.29%. Some problems of China's carbon emissions trading market are also concluded. The trading volume is small (only 43.4 × 106 t in 2020) and is discrepant greatly in the different ETS pilots. The carbon emissions price is low (only 33.45 Yuan/t in 2020), with high volatility and large price differences among the ETS pilots. Based on the existing problems, this paper proposes some suggestions for improving China's emission trading market and provides theoretical and decision-making support for promoting carbon emission reduction and neutralisation for China.

Warming promotes soil CO2 and CH4 emissions but decreasing moisture inhibits CH4 emissions in the permafrost peatland of the Great Xing'an Mountains

Permafrost peatlands, as large soil carbon pools, are sensitive to global warming. However, the effects of temperature, moisture, and their interactions on carbon emissions in the permafrost peatlands remain unclear, when considering the availability of soil matrixes. The permafrost peatland (0–50 cm soil) in the Great Xing'an Mountains was selected to explore the deficiency. The cumulative carbon dioxide (CO2) and methane (CH4) emissions from soil were measured under different temperatures (5 °C, 10 °C, and 15 °C) and moisture content (130%, 100%, and 70%) treatments by the indoor incubation. The results showed that the soil carbon and nitrogen matrix determined soil carbon emissions. Warming affected the availability of soil carbon and nitrogen substrates, thus stimulating microbial activity and increasing soil carbon emissions. With soil temperature increasing by 10 °C, soil CO2 and CH4 emission rates increased by 5.1–9.4 and 3.8–6.4 times respectively. Warming promoted soil carbon emissions, and the decrease of moisture content promoted CO2 emissions but inhibited CH4 emissions in the permafrost peatland. Soil moisture and the carbon and nitrogen matrix determined the intensity of CO2 and CH4 emissions. The results were important to assess soil carbon emissions from permafrost peatlands under the impact of future climate warming and to formulate carbon emission reduction policies.

Synergistic effect of carbon ETS and carbon tax under China's peak emission target: A dynamic CGE analysis

Global warming resulting from greenhouse gas emissions poses threats to humankind and has become a worldwide issue. As the top CO2 emitter in the world, China has committed to achieving its carbon emission peak by no later than 2030; in this context, how to best use and apply carbon emission reduction policy is particularly critical. By constructing a dynamic computable general equilibrium (CGE) model, we first examine a pure ETS included only the electricity sector in 2021, and the eight sectors starting in 2022, considering a declining carbon intensity rate of 4.5% and a higher rate of 4.8%. With the carbon intensity rates of 4.3% and 4.5%, we further evaluate two-hybrid systems of the carbon tax and carbon ETS, where the carbon tax of 10 yuan per ton is the starting levied rate in 2022 and increases at 4 yuan per ton year by year. The results proved that hybrid emission reduction policy can help reach a carbon emissions peak before 2030 and do so at a lower economic cost compared to the effect of pure carbon ETS. Besides, the coordinated use of a carbon tax and a carbon ETS can promote optimization of energy consumption structures and accelerate the decline of energy intensity and carbon intensity; this can contribute to curbing the growth of total energy consumption and total carbon emissions.

Green Finance, Chemical Fertilizer Use and Carbon Emissions from Agricultural Production

This study aimed to understand green finance’s impact on fertilizer use and agricultural carbon emissions. We selected the macro panel data of 30 provinces (cities) in China from 2000 to 2019. The main research methods are standardized test framework (cross-sectional dependence, unit root and cointegration test), the latest causal test, impulse response, and variance decomposition analysis. Examined the long-term equilibrium relationship between green finance, fertilizer use, and agricultural carbon emissions. The results show: fertilizer consumption and agricultural carbon emissions have a positive correlation. However, green finance can significantly reduce agricultural carbon emissions. The causal test confirmed the bidirectional causal relationship between agricultural carbon emissions and fertilizer use. At the same time, verified one-way causality from green finance to both of them. Interpret the results of impulse response and variance decomposition analysis: among the changes in agricultural carbon emissions, chemical fertilizers contributed 2.45%, green finance contributed 4.34%. In addition, the contribution rate of green finance to chemical fertilizer changes reached 11.37%. Green finance will make a huge contribution to reducing fertilizer use and agricultural carbon emissions within a decade. The research conclusions provide an important scientific basis for China’s provinces (cities) to formulate carbon emission reduction policies. China has initially formed a policy system and market environment to support the development of green finance, in 2020, the “dual carbon” goal was formally proposed. In 2021, the national “14th Five-Year Plan” and the 2035 Vision Goals emphasized the importance of green finance. It plays an important supporting role in carbon emission reduction goals, and green finance has become an important pillar of national strategic goals.

An evolutionary analysis of low-carbon technology investment strategies based on the manufacturer-supplier matching game under government regulations.

Developing a low-carbon economy is the only way for countries to achieve sustainable development. Carbon emission reduction policies and low-carbon technology (LCT) innovation play key roles in developing low-carbon economy. Under government reward and punishment regulations, based on the bilateral matching and evolutionary theories, this paper constructs an evolution model consisting of a manufacturer investing LCT and a supplier offering LCT to analyze multi-phase LCT investment strategies. Firstly, the profit optimization model of a green supply chain is constructed from the perspectives of centralized-matching (CM), decentralized-matching (DM), and mismatching (MM), and the spatial information internal evolution law of multi-phase LCT investment is described by the Markov chain. Then, a bilateral matching algorithm is proposed to solve the equilibrium solutions, and the evolution process of the three modes is analyzed by numerical simulation. Finally, based on the product green degree, we analyze the impact of subsidies and taxes on investment-production decisions. Analytical results show that the matching mechanism proposed in this paper can help supply chain firms to obtain stable matching and has a significant effect on the realization of "triple wins" of society, economy, and environment. The investment utility of CM is higher than that of DM and MM. Manufacturers are inclined to adopt LCT, and the investment level tends to be stable over time. Government reward and punishment regulations are helpful to motivate supply chain firms to invest in LCT, and the synergistic effect of subsidies and taxes is better than that of a single policy.

ANALYSIS OF THE SYNERGISTIC EFFECT OF CARBON TAXES AND CLEAN ENERGY SUBSIDIES: AN ENTERPRISE-HETEROGENEITY E-DSGE MODEL APPROACH

The application of clean energy is one effective way to alleviate the economy’s dependence on fossil energy while reducing greenhouse gas (GHG) emissions. However, the complementary and synergistic effects of environmental policy in clean energy promotion remain controversial. This paper aims to investigate the role of structural carbon taxes and clean energy subsidies in low carbon transition. Heterogeneous environmental dynamic stochastic general equilibrium (E-DSGE) approach with firm heterogeneity is applied to describe the impact path of structural environmental policy on China’s environmental-economic system. The results show that the structural carbon emission reduction policy has synergistic effects and can balance the relationship between energy demand and economic growth. Furthermore, distinguishing production technology and green innovation technology can promote energy-saving enterprises and improve the industrial structure. This paper might be the first attempt to discuss the synergistic effects of climate policy based on heterogeneous E-DSGE model.

Impact assessment of population migration on energy consumption and carbon emissions in China: A spatial econometric investigation

Human activities are closely related to energy consumption and carbon emissions. Nevertheless, in the context of energy transition and carbon emission reduction policies, the impact of population migration on energy consumption and carbon emissions is still unclear. Based on the panel data of 30 provincial regions in China from 2000 to 2019, this paper uses the spatial Durbin model to perform a spatial econometric investigation on this issue. In particular, this study innovatively embeds two spatial weight matrices, an economic gravity weight matrix and a population migration weight matrix, into the spatial Durbin model to capture the spatial spillover effects of population migration. The results show that population migration has obvious adverse impacts on the provinces with net outward population migration. Specifically, on the one hand, population migration increases energy consumption and energy poverty, but energy poverty is more severe in provinces with net outward population migration. On the other hand, population migration increases the carbon emissions and carbon reduction barriers of the provinces with net outward population migration and has no significant impact on the carbon emissions and carbon reduction barriers of the provinces with net inward population migration. Besides, the results of heterogeneity analysis show that population migration causes gaps in the energy structure among regions and even triggers the carbon transfer to provinces with net outward population migration. On the whole, the research results of this paper all prove that population migration increases the carbon reduction barriers in the provinces with net outward population migration. These empirical insights enrich the relevant theoretical achievements of the impact of population migration on energy consumption and carbon emissions and have a high reference value for formulating more reasonable regional energy transition and carbon emission reduction policies.

Path selection for carbon peaking and carbon neutralization based on uncertainty theory

Formulating reasonable policies to help achieve the goal of carbon peaking and carbon neutrality while ensuring economic growth is a key issue for the government since the goal was proposed. Combining with the relevant realistic background, we consider carbon emission reduction policies including green bond issuance and carbon emission allowances, analyze how the government should choose a method of strategic optimization to maximize corporate profits under the specified carbon emission reduction rate, and construct a related model of uncertain programming called optimal model of carbon emission reduction strategy, in order to obtain the optimal carbon emission reduction policies that the government should formulate under different circumstances while ensuring sustainable economic development.

Livelihoods, Technological Constraints, and Low-Carbon Agricultural Technology Preferences of Farmers: Analytical Frameworks of Technology Adoption and Farmer Livelihoods.

In the context of achieving carbon neutrality, it is scientifically important to quantitatively explore the relationships among livelihoods, technological property constraints, and the selection of low-carbon technologies by farmers to promote agricultural modernization and carbon neutrality in the agricultural sector of China. Based on the scientific classifications of farmer capital and low-carbon agricultural technologies, a farmer technology selection theory model considering capital constraints was developed in this study. Microcosmic survey data were collected from farmers in the Jiangsu province for empirical testing and analyses. A total of four low-carbon technologies related to fertilizer usage and three types of farmers’ livelihoods and their relationships were examined by using a logistic model. The results showed the existence of a significant coupling relationship between the intrinsic decision mechanism involved in selecting low-carbon agricultural technology and the properties of low-carbon agricultural technology for different types of farmers. Significant differences exist in the selection of different low-carbon technologies among large-scale farmers, mid-level part-time farmers, and low-level (generally small) part-time farmers. (1) When selecting technology, large-scale farmers are more inclined to accept capital-intensive, low-carbon technologies, such as new varieties, straw recycling, soil testing, and formulated fertilization. Mid-level part-time farmers are more inclined to accept capital intensive, labor saving, or low risk low-carbon agricultural technologies. In contrast, low-level part-time farmers are inclined to accept labor intensive technologies to reduce capital constraints and agricultural risks. (2) Large-scale farmers and low-level part-time farmers are influenced by household and plot characteristics, while mid-level part-time farmers are more influenced by plot characteristics. (3) Households with capital constraints created by differentiated livelihoods face challenges adopting capital-intensive low-carbon agricultural technologies, such as straw recycling, new varieties, soil testing, and formulated fertilization. However, farmers with stronger constraints in the areas of land and labor are more inclined to accept labor-saving technologies, such as soil testing and formulated fertilization technology. Moreover, farmers with stronger risk preferences tend to accept high-risk technologies, such as new technologies like straw recycling. The results of this study can provide a scientific basis for formulating carbon emission reduction policies and low-carbon technology policies for the agricultural sector.

Ecological Land Protection or Carbon Emission Reduction? Comparing the Value Neutrality of Mainstream Policy Responses to Climate Change

Improving the quality of forest, water, farmland, and other types of land use with outstanding ecosystem optimization, restoration functions (ecological lands) and reducing anthropogenic carbon emissions are recognized as the two main approaches of current mainstream climate change policies. The paper aims to evaluate and compare the value neutrality within these two main types of policy responses to climate change. To do that, a case study was conducted at the Yangtze River Economic Belt, China. We first summarized the implementation status of all climate change policies in the study area and collected data related to climate and economy at the policy pilot sites. Next, the coupling relationship between climate and socio-economic conditions at policy pilot sites was calculated by the Tapio model. Finally, we constructed dummy variables that reflected the status of policy implementation, to estimate the value neutrality of mainstream climate change policies and their impact on the coupling relationship by DID models. The results showed that the proportion of policies related to ecological lands that significantly improved the coupling degree between climate and socio-economic conditions of the pilot sites is more than that of carbon emission-related ones. Moreover, the average coupling degree between climate and socio-economic conditions of the pilot sites of ecological land policies was significantly increased by 3.99 units after policy implementation, which is 27.8% higher than that of carbon emission reduction policies. Generally, the two main findings directly evidenced that the climate change policies aimed at improving the area and quality of ecological lands were more conducive to the coupling development of the climate–economy nexus than the policies focusing on restricting carbon emissions, which provides important enlightenment for the establishment of relevant environmental policies around the world.