Industrial Carbon Emissions Research Articles

Carbon emissions in China's urban agglomerations: spatio-temporal patterns, regional inequalities, and driving forces.

Urban agglomerations are the centers of carbon emissions. However, research on sector-specific carbon emissions in different urban agglomerations is still limited. Drawing on the data of China's six urban agglomerations in 2005, 2010, and 2015, this study investigates the spatio-temporal patterns, regional inequalities, and driving forces of total, industrial, transportation, and residential carbon emissions. The study found that Beijing-Tianjin-Hebei was the total and sectoral emission center among the studied urban agglomerations. Additionally, regional carbon inequalities gradually decreased, implying a growing regional synergistic carbon pattern. The driving forces of carbon emissions, including population, GDP, energy intensity, secondary industry, tertiary industry, foreign investment, urbanization, and green coverage, varied across sectors and regions. Notably, foreign investment could lead to lower carbon emissions in less developed agglomerations like Beijing-Tianjin-Hebei, the Central Plains, and the middle reaches of the Yangtze River, whereas more developed agglomerations like the Yangtze River Delta and the Pearl River Delta benefited less from foreign investment. Besides, ChengYu has good ecological conditions and sustainable development modes, which linked urbanization and green space to reduced carbon emissions in the industrial sector. The findings can help formulate differentiated carbon policy and support sustainable development.

Deep Learning-Based Carbon Emission Forecasting and Peak Carbon Pathways in China’s Logistics Industry

As a major energy-consuming industry, energy conservation and emission reduction in the logistics industry are critical to China’s timely achievement of its dual-carbon goals of “carbon peaking” by 2030 and “carbon neutrality” by 2060. Based on deep learning, Random Forest (RF) was used to screen out the key factors affecting carbon emissions in the logistics industry, and the Whale Algorithm-optimized Radial Basis Function Neural Network (WOA-RBF) was proposed. The Monte Carlo simulation predicted the future evolution trends of each key factor under the three scenarios of baseline scenario (BAU), policy regulation scenario (PR), and technological breakthrough scenario (TB) and accurately predicted the carbon emission trends of the logistics industry from 2023 to 2035 by using the most probable future values of each influencing factor as inputs to the WOA-RBF prediction model. The results of the study demonstrate that fixed asset investment (LFI), population (P), total energy consumption (E), energy consumption per unit of value added of the logistics industry (EIL), share of oil consumption (OR), and share of railway freight turnover (RTR) are the key factors influencing the logistics industry’s carbon emissions. Monte Carlo simulations can effectively reflect the uncertainty of future changes in these key factors. In comparison to the BAU and PR scenarios, the TB scenario, with the combined incentives of national policy regulation and technology innovation, is the most likely for the logistics industry to meet the “Peak Carbon” goal baseline scenario.

Predictive modeling of carbon emissions in Jiangsu Province's construction industry: An MEA-BP approach

Understanding and predicting carbon emissions in the construction industry is crucial for sustainable development planning. In this study, we employed the GM(1,1) model and the Mind Evolution Algorithm (MEA) to optimize the BP neural network model and forecast carbon emissions in Jiangsu Province from 2022 to 2026. Using data related to energy consumption and building material usage in the Jiangsu construction industry from 1995 to 2021, we estimated carbon emissions using the life cycle assessment method and IPCC carbon emission coefficient method. Additionally, we utilized the STIRPAT model and Principal Component Analysis (PCA) to select and reduce important variables, identifying three principal components. Subsequently, the MEA-BP model was employed to provide detailed forecasts of carbon emissions over the next five years, revealing a declining trend in carbon emissions from the construction industry. The study thoroughly analyzed the direct and indirect impacts of population growth, economic development, and industrial restructuring on carbon emissions, proposing targeted policy recommendations including sustainable urban planning and green building design. Future research could enhance the applicability and accuracy of the model by expanding the dataset and considering more uncertainty factors. This research offers valuable insights into the trends and influencing factors of carbon emissions in the construction industry of Jiangsu Province, providing guidance for policymakers and future research directions.

Effect of surface water on wollastonite carbonation: Activated dissolution and mass transfer

Carbonation of non-hydraulic calcium silicate to produce cement represents a crucial strategy for mitigating carbon emissions in the cement industry. However, the dissolution rate of Ca2+ is the main constraint on the reaction kinetics. In this study, the carbonation of wollastonite (CS) was investigated, with an emphasis on the role of water in the Ca2+ dissolution process. Firstly, we investigated carbonation performance of CS and its influencing factors. Results revealed a significant impact of the water-to-solid (w-s) ratio on carbonation. In the carbonate products, we observed that the dissolution of Ca2+ adsorbed on the particle surface, referred to as “pseudocrystalline replacement”, which can serve as supplementary cementitious material. Additionally, this observation signifies the accordance of Ca2+ dissolution with the electric double layer (EDL) theory. Subsequently, we quantitatively assessed the influence of w-s ratio on carbonation using kinetic models. Based on EDL theory and the kinetic model, we categorized water film effect into three stages and determined the influence of water film thickness on the rate-determining factors of CS carbonation. This provides theoretical support for preparation of CS-based cement. Interestingly, we found that reaction rate constant varies with changes in the w-s ratio, indicating a catalytic role of water. Further analysis using density functional theory demonstrated that the free water combined with the O/Ca sites on CS to form hydroxylated surfaces, thereby decreasing and increasing the abilities of the local and neighboring sites, respectively, to absorb H2CO3. These findings provide a theoretical foundation for the curing method of low-carbon cements and offer a methodology for the preparation of supplementary cementitious materials.

Research on the impact of enterprise digital transformation on carbon emissions in the manufacturing industry

Reducing carbon emissions in the manufacturing industry is the key to achieving the “double carbon” goal, and digital transformation provides a new way to achieve carbon emission reductions. This research empirically dissects the impact of digital transformation on carbon emissions within the manufacturing industry, using data from publicly listed companies in China's manufacturing sector spanning from 2011 to 2019. Our findings reveal that enterprise digital transformation exerts a substantial mitigating effect on carbon emissions in the manufacturing industry. The underlying mechanism underscores that digital transformation can achieve this reduction by elevating the level of government green subsidies and facilitating technological advancements. Moreover, the digital transformation of manufacturing enterprises can influence manufacturing carbon emissions through electricity consumption, exhibiting resembling an inverted U-shape. Heterogeneity analysis shows that enterprise digital transformation significantly reduces carbon emissions in the central and western regions, but the impact on the eastern region is not significant.

The Impact of Import and Export Trade on Carbon Emissions: Based on The Perspective of Belt and Road Countries

In the context of economic globalization, environmental issues have gradually attracted people's attention, and the "Belt and Road" Cooperation Initiative (hereinafter referred to as B&R) proposed by China aims to promote economic development at the same time, the environmental role has become particularly important. This study uses linear regression model, two-stage least square (2SLS) method and event study method to analyze the relationship between international trade and carbon emissions from the perspective of B&R countries. The results are as follows: the influence of import and export trade on carbon emissions is generally positive.; The correlation between industrial structure and carbon emission in the B&R countries is low. Joining the B&R has increased the volume of international trade to certain extent, thus increasing the carbon emissions of B&R countries.

Study on industrial carbon emissions in China based on GDIM decomposition method and two decoupling effects.

The driving factors of China's industrial carbon emissions are decomposed by generalized Divisia index method (GDIM), so as to study the reasons for the change of China's industrial carbon emissions. The decoupling effect of China's industrial carbon emissions and economic growth is examined by speed decoupling and quantity decoupling. The speed decoupling is calculated by Tapio decoupling elasticity and emission reduction effort function, and the quantity decoupling is measured by environmental Kuznets curve (EKC). The results show that the positive driving factors are output size effect > industrial energy consumption effect > population size effect, and the negative driving factors are investment carbon emission effect > output carbon intensity effect > per capita output effect > economic efficiency effect > energy intensity effect. The elasticity of emission reduction is basically greater than that of energy conservation, indicating that there is still abundant room for efforts in emission reduction. The overall decoupling effect of carbon emissions is undecoupling-strong decoupling-undecoupling. Quadratic EKC shape is "U" shape, and the inflection point is 11.0987; the shape of cubic EKC is "N," and the inflection points are - 0.0137 and 2.4069, respectively, which satisfies the hypothesis of EKC curve.

Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends.

The carbon mitigation response encompasses a variety of strategies aimed at mitigating greenhouse gas emissions resulting from human activities. These measures are crafted to address the challenges posed by climate change and facilitate the transition of businesses towards a low-carbon paradigm. Leveraging the analytical outcomes of the extended STIRPAT model and the PSO-BP prediction model, this paper suggests countermeasures for reducing carbon emissions in China's metal smelting industry. The overarching objective is to contribute to China's attainment of the "dual carbon objectives." The study identifies key factors influencing carbon emissions in the metal smelting industry, ranked in descending order of sensitivity: population, coal consumption, urbanization rate, total metal production, carbon intensity, proportion of secondary industry, and GDP per capita. Results from three established scenarios-namely, low carbon, standard, and high carbon-indicate a consistent decline in carbon emissions from China's metal smelting industry over the next 15 years. This research not only enhances the findings of existing studies on carbon emissions in the metal smelting sector but also introduces an innovative approach to carbon emission reduction within China's metal smelting industry.

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction: A Case Study of Cities in the Pearl River Basin, China

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction: A Case Study of Cities in the Pearl River Basin, China

Research on Green Building Design Optimization Based on Building Information Modeling and Improved Genetic Algorithm

The energy consumption of the construction industry has been increasing year by year, posing a huge challenge to China’s dual carbon goals of peaking carbon emissions and achieving carbon neutrality. The Chinese construction industry has huge potential for energy conservation and emission reduction, and the government has therefore put forward requirements for constructing green buildings and formulated strict evaluation standards. The carbon emissions of the construction industry involve various stages of the entire life cycle and are closely related to the green building design standards that meet the requirements. This article sets multiple objective functions based on the two dimensions of the carbon emissions of the entire life cycle of buildings and green building evaluation and uses the NSGA-II algorithm in genetic algorithms to optimize ten indicators selected from the two objectives. Based on this, building information modeling (BIM) modeling was carried out for an office building project in Southwest China, and energy consumption analysis and evaluation were conducted based on the project’s multidisciplinary model. The dialectical relationship between the carbon emissions of the entire life cycle of buildings and the green building evaluation values was discovered, and the optimized parameter combination scheme corresponding to the Pareto solution set was obtained, providing a reference for using improved genetic algorithms and BIM technology to optimize green building design.

The driving factors of spatial differences on the whole life cycle carbon emissions of the construction industry: from the analysis perspective of total factor productivity

The energy saving and emissions reduction of the construction industry are crucial for China to achieve the “carbon peaking and carbon neutrality” goals. In order to promote the green development of the life cycle of the construction industry and improve the efficiency of emissions reduction. This paper examines the spatial-temporal distribution of life cycle carbon emissions in China’s construction industry (LCCECI) from 2004 to 2018. It uses the SBM-Malmquist total factor productivity (TFP) index to measure technological progress and establishes the spatial econometric model based on the STIRPAT model. The study investigates the driving factors of the LCCECI at the provincial and regional levels, aiming to provide suggestions for low-carbon development in the construction industry. The research results are as follows. ① The growth in the SBM-Malmquist TFP index of the construction industry distinctly curbs the LCCECI. ② Total population and urbanization level are not the primary driving factors for the LCCECI. The growth of per capita GDP significantly induces the LCCECI, while concurrently exhibiting a notable inhibitory effect on the LCCECI of neighboring regions. ③ The improvement of the SBM-Malmquist TFP index is conducive to the reduction of the LCCECI in the three major regions. The per capita GDP has the largest positive driving effect of the LCCECI in the eastern region, and the urbanization level the urbanization rate only significantly inhibits the growth of the LCCECI in the central region.

A four-party evolutionary game analysis of carbon emission reduction behavioral strategies in the shipping industry

Abstract Global warming and the urgency of environmental issues have brought the issue of carbon emissions in the shipping industry to the forefront. Taking the government, port enterprises, shipping companies, and social and environmental organizations as research subjects, this study delves into the key challenges of carbon emission reduction in the shipping industry. By constructing a complex and dynamic evolutionary game model and conducting data simulations with the help of MATLAB 2016b, we analyze in detail the strategic choices of each party on the emission reduction issue and explore the impact of these choices on the evolution of the system. In this study, we find that the government plays a crucial role in emission reduction regulation. Regulatory strategy and intensity have far-reaching impacts on the development of the whole system, and a flexible and intelligent regulatory system will promote active emission reduction by all parties. In addition, the reputation mechanism of social and environmental organizations plays a significant role in motivating all parties to take the initiative to adopt emission reduction strategies. In terms of economic incentives, we find that the government needs to provide differentiated support according to the characteristics of different enterprises, so that all parties can work together to move towards the emission reduction target. Through this study, we present a comprehensive game analysis of carbon emission reduction in the shipping industry and propose practical recommendations that are valuable to the shipping industry and environmental managers. These findings provide strong policy and action support for realizing the decarbonization of the shipping industry.

The Relationship Between Green Finance, Sustainable Technological Innovation and Energy Efficiency

Sustainable technological innovation can promote this progress in finance. This optimization and upgrading of energy efficiency can provide a good energy environment for green development. The collaborative innovation of financial enterprises has brought such development to the tertiary industry, driving the upgrading of consumer demand, and thus promoting the development of green finance. At this same time, sustainable technological innovation will increase investment in environmental protection, reduce energy consumption, promote the reduction of carbon emissions in the financial industry, and accelerate the development of green finance. This paper uses the data envelopment analysis method to calculate the Marquis production efficiency index, examines the dynamic generation efficiency of intertemporal energy input and output, and calculates the energy efficiency of green finance. This article measures and deconstructs energy efficiency at the provincial level, and pays attention to its changes. The study depicted the efficiency distribution of 28 provinces in two stages of the survey period. By using random kernel estimation for two periods during the survey period, dynamic distribution three-dimensional maps and density contour maps of total factor energy productivity, energy utilization efficiency, and energy allocation efficiency growth rates were drawn for the two periods. The results show that the transfer probability group of energy efficiency and its decomposition terms mainly falls near the diagonal, indicating that TFP and its decomposition term growth rate have certain transferability. From 2005 to 2015, the energy utilization efficiency of the seven economic regions has been significantly improved, and the energy efficiency differences in different regions have gradually converged. The energy efficiency of the Yangtze River Delta and the Pearl River Delta is the highest and has been continuously improved, followed by the Beijing Delta, the central region, the northeast region and the southwest region are again in terms of energy efficiency. Compared with provinces with relatively poor industrial structures, provinces with better industrial structures do not have significant advantages in energy efficiency, while provinces with higher levels of technological innovation typically have relatively higher energy efficiency.

Can industrial co-agglomeration improve carbon emission efficiency? Empirical evidence based on the eastern coastal areas of China.

The goal of "carbon peak and carbon neutrality" is the key to coping with global warming and achieving high-quality development. Producer services and manufacturing co-agglomeration (Coagglo) is an important path to achieve low-carbon development. Therefore, the relationship between industrial co-agglomeration and carbon emission efficiency (CEE) needs to be discussed. Based on the panel data of 114 cities along the eastern coast of China from 2006 to 2021, this study uses a panel quantile regression model and dynamic spatial Durbin model to evaluate the impact and spatial effect of Coagglo on CEE. The results show that there is a nonlinear relationship between Coagglo and CEE. When it exceeds the 50th quantile, the degree of influence decreases slightly, but it still shows a significant positive correlation. When considering industry heterogeneity, we find that the co-agglomeration of warehousing and postal industry (TRA) and manufacturing has the most significant impact on CEE, while the co-agglomeration of leasing and commercial service industry (LEA) and manufacturing has the least impact on CEE. Regional heterogeneity shows that the Coagglo has a greater impact on carbon emission efficiency in the northern region than in the southern region. In addition, Coagglo promotes the spillover of knowledge and technology and has a positive spatial spillover effect on CEE. This conclusion provides a theoretical reference for carbon emission reduction in eastern coastal areas of China.

Simulation of fishery energy consumption and carbon emissions in Zhoushan City based on the long-range energy alternatives planning system model

To understand the current situation and development trend of energy consumption and carbon emissions in the fishery industry, a Long-Range Energy Alternatives Planning System (LEAP)-fishery model for Zhoushan City was constructed, and the trends of energy consumption and carbon emissions in the Zhoushan City fishery industry were analyzed under the business-as-usual scenario, the low-carbon scenario, and the strengthened low-carbon scenario. The results show that under the business-as-usual scenario, the future energy consumption of Zhoushan City’s fisheries in general shows an increasing trend, and the composition of energy consumption in all scenarios is primarily dominated by fossil energy, with diesel and coal predominating. In terms of the structure of end-use energy consumption, fishing vessels and aquaculture consume the most energy, with energy consumption exceeding 30% of total energy consumption in all scenarios. In terms of the emission reduction contribution rate, which the promotion of clean energy generation, the improvement of energy efficiency, and the reduction of aquaculture production play key roles in the low-carbon development of the fishery industry. From an economic perspective, carbon reduction measures such as reduction of aquaculture production and increased efficiency of energy delivery would have significant economic benefits, but these measures have limitations. Finally, suggestions are made to optimize the energy structure, improve the implementation of energy intensity and total energy control, and strengthen public awareness of energy conservation to reduce carbon emissions from the fishery industry, thus making the research in this paper relevant to real-world situations.

Evaluation for the effect of low-carbon city pilot policy: evidence from industry in China.

The investigation of the implementation effect of the low-carbon city pilot (LCCP) policy in promoting industrial low-carbon development is important for economic growth and carbon reduction in China. This paper takes the industry sector as an independent investigation object, and aims to examine the effectiveness of the policy in improving industrial low-carbon development. The industrial total factor carbon emission efficiency index (ITFCEE) is constructed to measure the level of industrial low-carbon development by the global frontier directional distance function. From the perspective of the multiple participants of the policy, we analyze the mechanisms of the policy, and construct the time-varying difference-in-difference model to test how LCCP policy impacts the ITFCEE. We conclude that (1) LCCP policy improves significantly the ITFCEE. (2) Heterogeneity analysis results show that the positive influence of LCCP policy is significant in cities with high industrial carbon emission intensity, high industrial structure upgrading, and high government expenditure. (3) The result of the mechanism analysis is that LCCP policy can benefit the ITFCEE by promoting enterprise green technology innovation, public environment concern, government technology support, and administrative regulation.

Research on the coal saving and emission reduction potential of advanced technologies in China's iron and steel industry

The coal-based energy consumption structure has led to high coal consumption and carbon emissions in China's iron and steel industry, and the task of coal saving and carbon reduction in the steel industry is difficult and urgent. The economic viability of various coal-saving and emission-reduction technologies, the extent of their coal-saving and emission-reduction potential, and the guidance on the adoption of advanced technologies are crucial issues that need to be addressed in the Chinese steel industry. This article employs the Energy-Production (E-P) analysis method and the Conservation Supply Curve (CSC) model incorporating coal consumption ratio coefficients to systematically analyze coal consumption and carbon dioxide emissions in the overall iron and steel industry and its sub-processes under different scenarios. It calculates the coal-saving emission reduction costs of 32 technologies, evaluates them comprehensively based on their coal-saving and emission-reduction potentials and cost-effectiveness, and categorizes them accordingly. According to the cost of coal saving and emission reduction as the demarcation point, a total of 19 technologies are cost-effective; according to the coal-saving emission reduction potential and cost division, 32 technologies are divided into 6 categories, among which 6 technologies such as sintering waste heat recovery technology can be used as key technologies for popularization and application. Seven technologies, such as efficient preheating technology of ladle, have high cost and low potential, which can be gradually eliminated or improved and upgraded. After the comprehensive promotion of the 32 technologies, the maximum coal saving potential is about 3.453 billion GJ, the CO2 emission reduction potential is 395 million tons, the total coal consumption of the steel industry can be reduced to about 13.904 billion GJ, and the carbon dioxide emissions can be reduced to 1.179 billion tons.

Study on carbon emission driving factors and carbon peak forecasting in power sector of Shanxi province.

The realisation of the low-carbon transition of the energy system in resource-intensive regions, as embodied by Shanxi Province, depends on a thorough understanding of the factors impacting the power sector's carbon emissions and an accurate prediction of the peak trend. Because of this, the power industry's carbon emissions in Shanxi province are measured in this article from 1995 to 2020 using data from the Intergovernmental Panel on Climate Change (IPCC). To obtain a deeper understanding of the factors impacting carbon emissions in the power sector, factor decomposition is performed using the Logarithmic Mean Divisia Index (LMDI). Second, in order to precisely mine the relationship between variables and carbon emissions, the Sparrow Search Algorithm (SSA) aids in the optimisation of the Long Short-Term Memory (LSTM). In order to implement SSA-LSTM-based carbon peak prediction in the power industry, four development scenarios are finally built up. The findings indicate that: (1) There has been a fluctuating upward trend in Shanxi Province's total carbon emissions from the power industry between 1995 and 2020, with a cumulative growth of 372.10 percent. (2) The intensity of power consumption is the main factor restricting the rise of carbon emissions, contributing -65.19%, while the per capita secondary industry contribution factor, contributing 158.79%, is the main driver of the growth in emissions. (3) While the baseline scenario and the rapid development scenario fail to peak by 2030, the low carbon scenario and the green development scenario peak at 243,991,100 tonnes and 258,828,800 tonnes, respectively, in 2025 and 2028. (4) Based on the peak performance and the decomposition results, resource-intensive cities like Shanxi's power industry should concentrate on upgrading and strengthening the industrial structure, getting rid of obsolete production capacity, and encouraging the faster development of each factor in order to help the power sector reach peak carbon performance.

Carbon emission scenarios of China's construction industry using a system dynamics methodology – Based on life cycle thinking

China is currently one of the world's major energy consumers and CO2 emitters. To save energy, and reduce consumption and carbon emissions, China proposed (at the 2020 United Nations General Assembly and Climate Summit) the introduction of stronger policies and measures for CO2 emissions to peak in 2030 and to achieve carbon neutrality by 2060. The construction industry is a major contributor to China's carbon emissions, thus research on energy saving and carbon reduction in this industry is essential. The construction industry is characterized by complex upstream and downstream industrial chains, with different energy consumption at each stage, and a long overall life cycle. We used life-cycle thinking (LCT) to analyze the carbon emissions of the whole life cycle of the construction industry and built a model by using a system dynamics method, which analyzes the carbon emission process of the construction industry at different stages. Combined with the planning and policies implemented by the construction-related departments, we identified the main indicators of policy regulation and control. We used sensitivity analysis to examine the impacts of factors of regulation and control. We also adjusted key indicators and set up different scenarios to simulate the carbon emissions based on the effort to achieve “peak carbon.” The results show that the carbon emissions of the construction industry will be reduced by 2060, achieving the goals of “peak carbon” and “carbon neutrality.” Although the construction and operation stages individually can achieve peak carbon by 2030, the whole process of the construction industry will reach peak carbon by 2045, 2038, or 2036, depending on specific aspects of the scenario considered. However, the indicators, such as the green building ratio can realize carbon emission reduction at building operation stage for 4%∼6%, but cause greater carbon emission for 5%∼7% at construction material production and transportation stage—a phenomenon called “policy island.” Therefore, the coupling of policies should be a key concern in policy formulation and implementation.

Embodied Carbon Emissions in Shanghai's Power Industry Based on Input-Output Analysis

Embodied Carbon Emissions in Shanghai's Power Industry Based on Input-Output Analysis