Industrial Carbon Emissions Reduction Research Articles

Prediction of China's industrial carbon peak: Based on GDIM-MC model and LSTM-NN model

The industrial sector is the key area for China to achieve the carbon peaking goals, as it accounts for more than 65 % and 70 % of the national total energy consumption and carbon emissions. However, the discussion on the time and route of carbon peak in China in the existing literature is still quite different. In this study, we establish three scenarios and comprehensively used Monte Carlo simulation and LSTM Neural Network model to predict the evolution trends of China's industrial carbon emissions during 2020–2030. Firstly, the decomposition results of the Generalized Divisia Index Method shows that fixed assets investment is the most important factor for promoting and carbon intensity of investment is the key for reducing carbon emissions. Then, basing on the Monte Carlo dynamic simulation, we could draw the three kinds of carbon emissions route that it will peak in 2031 in the Baseline scenario, in the Green Development scenario (environmental policy improvement) and Technological Breakthrough scenario (green technology progress) will peak in 2027 and 2025, and under the LSTM Neural Network model, peak time will occur in 2028. Comparing the results of above predictions, China's industrial carbon emissions could peak by 2030(in GD scenario, 2027; TB scenario, 2025). Finally, we discuss the path of China's industrial carbon emissions reduction and provide a reference for the rational formulation of low-carbon regulatory policies in the future and the realization of sustainable development.

Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.

Research on industrial carbon emission prediction and resistance analysis based on CEI-EGM-RM method: a case study of Bengbu

This paper focuses on the development trend of industrial carbon emissions in Bengbu city, Anhui Province in the next ten years, and how to help the industry reach the carbon peak as soon as possible. The research process and conclusions are as follows: (1) Through literature review and carbon emission index method, five main factors affecting industrial carbon emission are identified. (2) The resistance model is used to analyze the main resistance factors of industrial carbon emission reduction in Bengbu city. (3) Based on the existing data of Bengbu city from 2011 to 2020, the grey prediction EGM (1,1) model is used to predict the industrial carbon emissions of Bengbu city from 2021 to 2030. The results show that among the five factors, the urbanization rate has the most significant impact on industrial carbon emissions, while energy intensity has the least impact. Bengbu’s industrial carbon emissions will continue to increase in the next decade, but the growth rate will be flat. Based on the findings of the analysis, specific recommendations on urbanization development, energy structure, and industrial structure of Bengbu city are put forward.

Spatiotemporal heterogeneity and decoupling decomposition of industrial carbon emissions in the Yangtze River Delta urban agglomeration of China.

The objective of this study is to identify the spatiotemporal change law and the leading factors of industrial carbon emission decoupling. Based on the industrial carbon emission level of the Yangtze River Delta urban agglomeration (YRDUA) from 2006 to 2020, the spatiotemporal heterogeneity was explored with the help of the spatial Markov chain, the Tapio decoupling model was used to analyze its decoupling state from the industrial economy, and its driving factors were decomposed using the Kaya identity and logarithmic mean Divisia index (LMDI) model. The results show that (1) in 51.9% of the YRDUA's cities, the industrial carbon emission situation was stable, the emission reduction observation area (medium carbon) occupied a dominant position, and the emission reduction key area (relatively high carbon) gradually decreased. (2) Industrial carbon emissions had spatial overflow and path dependency characteristics, and the probability of carbon emission type transfer maintaining the original state reached 80.0%. From 2006 to 2011, the average probability of the downward migration of high-carbon cities was 5.0%. From 2011 to 2020, the average probability of the upward transfer of low-carbon cities was 9.4%. (3) The negative decoupling rate of carbon emissions in the YRDUA experienced a transition from 3.7% to 44.4% and then back to 7.4%, showing spatial imbalance. Unsatisfactory decoupling cities were concentrated along the Yangtze River and in coastal areas. (4) The promoting efficiency of energy intensity, carbon emission coefficient, and employment structure was gradually strengthened, and the carbon-increasing effect of labor input was gradually weakened. (5) The decoupling mode of heavy difficult cities is dominated by the three-factor balanced type, which is jointly affected by industrial production, labor input, and carbon emission coefficient. The findings in this study can provide inspiration for industrial carbon emission reduction in megalopolises.

Decoupling analysis of economic growth, energy consumption and CO2 emissions in the industrial sector of Guangdong Province

Abstract Industrial carbon emissions are the main part of China’s carbon emissions, so industrial carbon emission reduction is an important tool to achieve the ‘double carbon’ target. This paper constructs a Tapio model to decompose the total decoupling indicators into energy-saving decoupling indicators and emission-reduction decoupling indicators, with a view to revealing more deeply the reasons and mechanisms for the changes in the decoupling indicators between economic growth and CO2 emissions in the industrial sector of each prefecture-level city in Guangdong Province, as well as using cluster analysis to further explore the changes in the effect of energy saving and emission reduction in the industrial sector over the three five-year plans. The conclusions showed that: ( 1) the overall efficiency of energy saving and emission reduction in the industrial sector of Guangdong Province has improved, but there are large differences in efficiency between different cities and ( 2) the energy structure of the industrial sector in Guangdong Province has changed little due to immature technology. Based on the aforementioned findings, this paper puts forward corresponding policy recommendations.

Driving Effect of Decoupling Provincial Industrial Economic Growth and Industrial Carbon Emissions in China.

China is facing the dual challenges of fostering economic growth and mounting an effective response to climate change, so it is vital to continue promoting industrial carbon emission reduction. This paper uses panel data from 1998 to 2019 to measure the industrial carbon emissions of 30 provinces in China. The Tapio decoupling and IPAT (Impact = Population × Affluence × Technology)-based decoupling models are used to analyze each province's velocity and quantity decoupling index for industrial carbon emissions. The fixed effect model analyzes the influencing factors for carbon decoupling. The results show that the industrial carbon emissions of various provinces in China are increasing yearly, but there are significant differences among provinces. The carbon decoupling of the industrial economy in most provinces is weak, and the quantitative decoupling index is better than the velocity decoupling index. The cleanliness of energy, balance, and labor productivity significantly affect the velocity decoupling index. The cleanliness of energy, the industry's structure, and the population significantly affect the quantity decoupling index. Based on empirical results, the study puts forward some policies to promote the efficient carbon decoupling of the industrial economy.

Study of the Effect of China’s Emissions Trading Scheme on Promoting Regional Industrial Carbon Emission Reduction

The creation of carbon emissions trading markets is a core policy for realizing China’s twin objectives of reaching a peak in CO2 emissions before 2030 and achieving carbon neutrality by 2060. Given that industry is the most significant energy consumer and CO2 emitter, it is imperative to implement carbon reducing initiatives to attain these goals. Following the implementation of carbon emissions trading pilots in China, this article theoretically analyzes the mechanisms of action and paths of influence of China’s carbon trading policies on regional industrial carbon emissions. Then, regarding the trading rights policies launched in 2013 as a quasi-natural experiment, this study uses provincial panel data and industry data from 2003 to 2016 to empirically test the effect of carbon trading on industrial emissions by employing the difference-in-difference and difference-in-difference-in-difference methods. It was found that carbon emissions trading can promote a reduction in regional industrial carbon emissions, achieving the dual aims of reducing total emissions and reducing emission intensity. The reduction effect occurs after the implementation of the carbon trading market policies. The carbon trading policies reduced regional industrial emissions by optimizing regional industrial structures and increasing regional technological innovation. It was also found that reductions in carbon emissions were heterogeneous among industries. These research conclusions will help to improve the top-level design of China’s industrial energy saving and carbon reduction policies and to achieve low-carbon and green industrial development.

Carbon peaking strategies for industrial parks: Model development and applications in China

Industrial carbon emission reduction is an important target for most countries. China pledges to achieve carbon dioxide peaking and neutrality before 2030 and 2060 respectively where industrial parks agglomerate most of the manufacturing industries and contribute much to the total CO2 emission; thus, it is of great significance to explore appropriate CO2 peaking strategies for the parks. We develop a land-industry-carbon integrated model that jointly projects land availability, land productivity, industrial structure, CO2 emissions, and carbon productivity (the ratio of gross industrial output value to CO2 emission) of industrial parks to quantitatively elucidate the CO2 peaking strategies. This study applies the model to four typical medium and large-sized Chinese industrial manufacturing parks. We analyze the characteristics of energy consumptions and CO2 emissions and reveal the parks have CO2 reduction potential ranging from 52% to 73% driven by carbon productivity improvement and industrial structure adjustment. Two parks could peak CO2 emissions before 2025 and realize the absolute decoupling of economic development from CO2 emissions during 2020–2030. Their carbon productivities will be 1.54 and 1.61 times in 2025 and both 2.78 times in 2030 compared with those in 2020. Another two parks tend to step from relative decoupling during 2020–2025 to absolute decoupling later, and their CO2 emissions will peak before 2030 with 2.08- and 3.70-times carbon productivities as much as those in 2020. Based on the model applications, two synergistic carbon peaking pathways are proposed for Chinese industrial parks, including (1) reallocating CO2 caps among industries considering land and carbon productivities, (2) facilitating the carbon productivity improvement for the stock industries and setting thresholds of carbon productivity for the forthcoming incremental industries. Customized strategies are also developed for the four parks. The model can provide both a pragmatic paradigm for industrial carbon peaking and policy support for global industrial parks.

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.

Does the green credit policy reduce the carbon emission intensity of heavily polluting industries? -Evidence from China's industrial sectors

Green credit policy (GCP) is an important practical exploration to guide green economic development by financial means. Empirically, however, little is known about the relationship between GCP and industrial carbon emissions intensity (CEI). This study aims to investigate the impact of GCP on the CEI of heavily polluting industries (HPIs) by treating Green Credit Guidelines as a quasi-natural experiment. Using Chinese industry-level panel data and a difference-in-difference model, we find that after the implementation of GCP, the CEI of HPIs decreased by an average of 0.267 tons/104 yuan per year compared to non-HPIs. Resource allocation effect and green innovation effect are two channels through which GCP reduces CEI of HPIs. Moreover, the GCP has a greater effect on the CEI of HPIs with lower state-owned ratios, higher total factor productivity and higher capital dependence. These findings provide policy insights for promoting industrial carbon emissions reduction.

Collaborative Governance Mechanism of Climate Change and Air Pollution: Evidence from China

Since entering the industrialized era, China’s greenhouse gas emissions and air pollutant emissions have increased rapidly. China is the country with the most greenhouse gas emissions, and it is also facing serious local air pollution problems. China’s industrial sector is the largest contributor to CO2 and air pollutants. The resulting climate change and air pollution issues have caused China to face double pressures. This article uses the CO2 and comprehensive air pollutant emission data of China’s industrial sector as a starting point and uses econometric research methods to explore the synergy between China’s industrial carbon emission reduction and industrial comprehensive air pollutant emission reduction. The synergistic effect between industrial carbon emissions and industrial comprehensive air pollutant emissions has been quantified, and the transmission path of the synergistic effect has been explored. The empirical results show that there are benefits of synergistic governance between climate change and air pollution in China’s industrial sector. Every 1000 tons of carbon reduction in the industrial sector will result in 1 ton of comprehensive air pollutant reduction. The increase in R&D expenditure in the energy and power sector can significantly promote the reduction of air pollutants in the industrial sector. Increasing the intensity of environmental regulations is the main expansion path for synergy. However, in eastern, central, and western China, the synergy is not the same. Therefore, it is necessary to formulate regionally differentiated emission reduction policies. The research conclusions of this article can provide policy references for the coordinated governance of climate change and air pollution in China.

Production Decision Optimization for Iron and Steel Scrap Remanufacturing considering Carbon Emission and Delivery Time

Under the requirements of the national carbon reduction target, the recycling and remanufacturing of scrap iron and steel are taken by major iron and steel enterprises as requirements for implementing industrial carbon emission reduction and are incorporated into their production strategies. The scheduling optimization of scrap iron and steel remanufacturing processes plays an important role in the energy saving and emission reduction of iron and steel enterprises. In this paper, a remanufacturing production decision model for scrap iron and steel considering both the product delivery time and carbon emissions was established, and a discrete krill swarm algorithm was designed to solve the multiobjective production decision model. The effectiveness of the model and the algorithm was verified by an example, demonstrating a good decision-making reference for the implementation of energy conservation and emission reduction in iron and steel enterprises.

The driving factors in the corporate proactivity of carbon emissions abatement: Empirical evidence from China

The abatement of carbon emissions is an important way to alleviate global warming. It is therefore crucial to explore the factors behind the corporate proactivity of carbon emissions abatement (CPCEA). However, existing research mainly focused on the factors that promote national, provincial, city-level, and industrial carbon emissions reduction, without looking at the micro-company level. In response to the research gap, this study therefore explored the drivers of CPCEA using panel data from 736 listed companies in China. This study used the logit regression to examine the effects of pre-identified factors on the CPCEA. The results indicated that political ties, corporate scale, industry category, and regional disparity have significant impacts on the CPCEA. Contrary to expectations, environmental regulation was not found to affect CPCEA. Based on the findings of this study, five policy implications to improve CPCEA in China were proposed. This paper presented empirical evidence to promote CPCEA. Additionally, the results have important practical implications and priority guide for carbon emissions reduction in China and around the world.

Recovery of organic matter from pharmaceutical waste water by energy-saving complex distillation column

ABSTRACT An energy-saving complex distillation column, can separate ethyl acetate and isopropanol from pharmaceutical waste water. This allows reducing energy consumption and improve economically efficiency, was proposed by this article. According to the requirements of industrial carbon emission reduction, carbon tax and carbon emission rate, were added to the calculation of total annual cost (TAC). Based on Aspen Plus simulation, carbon emission reduction as a comprehensive optimization objective to optimize the process. The optimization results show that process is more energy-saving than traditional processes, the reboiler duty and TAC are respective reduced by 44.96% and 52.93%, and the carbon emission reduction is calculated accurately. This study provides the design and theoretical basis for the industrialization of similar organic matter separation process.

A historical time series for inter-industrial embodied carbon transfers within China

China has made ambitious carbon emission reduction commitments at the UN Climate Change Conference in Paris in 2015. However, the policies that have been adopted are based on the production accounting principle and are excessively concentrated in high and direct carbon intensity industries. This production-based accounting can lead to unfair responsibility for emissions reduction, mismatch in economic development, and carbon leakage. Embodied CO2 emissions depict the accumulated emissions generated in the production process of a product, and it is based on the consumption accounting principle. This accounting method addresses some shortcomings on production responsibility, thus providing a new perspective for carbon emission reduction. Based on 1992–2012 input-output tables and direct carbon emissions of China, this study calculated the embodied carbon transfer within 28 industries. A visualized dynamic embodied carbon database was constructed, covering all industries in China within a 20-years period. We characterized the embodied carbon transfer structure among different industries in China. The main embodied carbon input and output sectors were identified, and targeted industry emission reduction recommendations are provided. The power industry (an upstream industry), and the construction industry (a downstream industry) were considered typical net export and import industries. These sectors are suitable for formulating emission reduction policies from their respective supply and demand sides. The chemical industry is in the middle of the industrial chain, where the input and output are equal. Therefore, policies that affect both supply and demand can achieve better results. This paper provides detailed data to support China’s overall arrangement of industrial carbon emission reduction.

Optimization of Inter-industry Carbon Emission Transfer Structure Based on Decoupling Effects: an empirical analysis from Chinese industry

This paper utilizes 27 Chinese industry sectors as study objects, and measures the decoupling effects of the industrial carbon emission transfer; furthermore, this paper proposes several optimizing strategies for an inter-industry carbon emission transfer structure toward the realization of dual goals, i.e., industrial economic development and industrial carbon emission reduction. The research result shows that: Different industries show different decoupling effects of carbon emission transfer, and the distribution of the six states of decoupling effects could all be observed. With respect to dynamic variations, the decoupling effects of industrial carbon emission transfer were optimal during the late stage, and the decoupling effects during 2010-2012 were the most optimal. With respect to the optimizing strategies for the inter-industry carbon emission transfer, efforts should be focused on increasing the carbon emission exports, while reducing the carbon emission imports of 11 industries (CMWI and others). Future efforts should be directed at increasing carbon emission imports and reducing carbon emission exports in the three industries of CLII, PPSM, and MPI, while increasing both carbon emission exports and imports in 10 industries (FMTP and others), and reducing both carbon emission imports and exports in the three industries of MMII, OMW, and WRT.

Inter-industrial Carbon Emission Transfers in China: Economic Effect and Optimization Strategy

Understanding inter-industrial carbon emission transfers and their economic effect informs approaches to achieve emission reduction objectives and promote industrial economic development. This paper applies input-output theory to explore ways to optimize carbon emission transfers between industrial sectors. First, China's inter-industrial carbon emission imports and exports were measured for years 2002, 2005, 2007, and 2010. Next, the economic effects of inter-industrial carbon emission transfers were assessed. Finally, strategies to optimize the carbon emission transfer structure were proposed, with the goal of achieving a win-win between industrial carbon emission reduction and economic development. Key study conclusions are as follows. (1) Inter-industrial carbon emission imports and exports in China are significant, and are increasing each year. Traditional energy industries have high carbon emission imports; processing and manufacturing industries have high carbon emission exports; and most light industries have relatively low levels of both carbon emission imports and exports. (2) Carbon emission transfer imports or exports can promote industrial development; combining both imports and exports leads to variable economic effects within specific industries. (3) To achieve the dual goals of carbon emission reduction and economic development, four strategies are proposed to optimize carbon emission transfer structures in different industries.

Decoupling and Decomposition Analysis of Carbon Emissions from Industry: A Case Study from China

China has overtaken the United States as the world’s largest producer of carbon dioxide, with industrial carbon emissions (ICE) accounting for approximately 65% of the country’s total emissions. Understanding the ICE decoupling patterns and factors influencing the decoupling status is a prerequisite for balancing economic growth and carbon emissions. This paper provides an overview of ICE based on decoupling elasticity and the Tapio decoupling model. Furthermore, the study identifies the factors contributing to ICE changes in China, using the Kaya identity and Log Mean Divisia Index (LMDI) techniques. Based on the effects and contributions of ICE, we close with a number of recommendations. The results revealed a significant upward trend of ICE during the study period 1994 to 2013, with a total amount of 11,147 million tons. Analyzing the decoupling relationship indicates that “weak decoupling” and “expansive decoupling” were the main states during the study period. The decomposition analysis showed that per capita wealth associated with industrial outputs and energy intensity are the main driving force of ICE, while energy intensity of industrial output and energy structure are major determinants for ICE reduction. The largest contributing cumulative effect to ICE is per capita wealth, at 1.23 in 2013. This factor is followed by energy intensity, with a contributing cumulative effect of −0.32. The cumulative effects of energy structure and population are relatively small, at 0.01 and 0.08, respectively.

Investing in finite-life carbon emissions reduction program under risk and idiosyncratic uncertainty

This paper aims at emphasizing the ability of new frameworks of real option model to highlight key characteristics of industrial Carbon Emissions Reduction Program investment decision. We develop both theoretical arguments and numerical simulations with structural parameters calibrated on real-life data. We find that both radical uncertainty and risk lead to speed-up green investments, compared to the predictions of real option models that are normally used in green investment literature. The conventional “wait and see” attitude, questioned in recent developments of the real option theory, is not validated. In conclusion, our results should foster companies to implement green investments and help governments to define appropriate incentives to encourage green investments. Of particular note, the paper highlights that finance theory is not necessarily an obstacle to green investment decisions.