Industrial Carbon Emissions Research Articles

Study on the Influence of Density and Water–Cement Ratio on the Cement Utilization, Fluidity, Mechanical Properties, and Water Absorption of Foam Concrete

In this paper, we study the influence of density and the water–cement (W/C) ratio on the slurry fluidity, compressive strength, and water absorption of foamed concrete (FC) and its mechanism of action, with the aim of proposing an optimal mix ratio for FC to reduce cement usage and carbon emissions in the construction industry and ensure the good overall performance of FC. In this experiment, two groups of experiments were designed using the control variable method. Fluidity and uniaxial compression tests showed that when the density was 600 kg/m3 and the W/C ratio was 0.6, the FC slurry had maximum fluidity, but its mechanical properties were poor and it collapsed easily. Conversely, by analyzing the uniaxial compressive strength/cement (UCS/C) ratio, it was observed that the mix ratio had a maximum cement utilization rate (W/C ratio) of 0.5 and a density of 1000 kg/m3. Nondestructive testing methods were used to measure the ultrasonic pulse velocity (UPV) and rebound value of the FC test block, and the strength and durability of FC were analyzed. The water absorption rate of the FC test block was tested, and the final analysis showed that the optimal mix ratio of FC in this test was W/C = 0.5, with a density of 1000 kg/m3.

Analysis of Low-Carbon Contribution Pathways for Resource-Based Cities under the Dual Carbon Goals: The Case of Tangshan, China

China is still the biggest carbon emission entity at present, which is mostly produced by resource-based cities. It means that actions to control carbon emissions in order to mitigate the greenhouse effect are urgently needed. The purpose of this study is to examine resource-based cities' low-carbon development strategies after the dual carbon regulations were put into place. Using a literature review as the research technique, this paper uses Tangshan as an example to explore the current condition, difficulties that exist, and Policy recommendations for resource-based cities looking to improve in a low-carbon manner. The result shows that there are now some clear issues with resource-based cities' low-carbon growth routes: high reliance on high-emission industries, the incomplete top-level design, and “the intensifying competitive pressure from surrounding cities. The following optimization suggestions have been proposed for the above-mentioned issues. The optimization direction of resource-based areas should be to clarify the top-level design, increase competitiveness, and continue to increase industrial carbon emission reduction. In other words, this kind of cities should reduce the dependence of urban development on high-polluting industries and attract talent to cope with external competitive pressures requires the government to coordinate and cooperate across departments, so that under the dual carbon goals they can achieve carbon reduction in resource-based cities which are under the dual carbon goals.

Technology and material-oriented strategies can reduce lead industry carbon emissions in China

Achieving carbon neutrality in the lead industry requires both technological decarbonization and comprehensive reduction of surplus lead to mitigate environmental risks from stacking. A successful decarbonization pathway requires integrating material-oriented and technology-oriented factors synergistically. In this study, a comprehensive dynamic model was developed to encompass emissions of greenhouse gases throughout various processes and levels of lead redundancy across different scenarios. The results show that China’s cumulative lead consumption amounts to 190.24 million tonnes, with greenhouse gases emissions reach 250.96 million tonnes CO2-equivalent between 2021 and 2060. Strategies focused on material-oriented approaches to achieve an earlier carbon peak can yield short-term success, while a technology-oriented strategy demonstrates superior long-term optimization. In order to achieve synergistic outcomes in reducing pollution and greenhouse gas emissions, it is imperative to prioritize short-term improvements in recycling rates and long-term technological advancements. Additionally, exploring new applications for surplus lead should also be pursued.

Mitigating carbon emissions in the taxi service industry: a comprehensive Life Cycle Assessment of batteries for electric taxis and identification of a greener vehicle fleet

Mitigating carbon emissions in the taxi service industry: a comprehensive Life Cycle Assessment of batteries for electric taxis and identification of a greener vehicle fleet

Green Energy Transition: Evaluating Carbon-To-Liquid Technologies for 2050 Climate Goals and Energy Security

China's coal-to-liquid (CTL) sectors must quickly adjust to a low-carbon economy to meet the energy demand while mitigating environmental and energy security challenges. Similarly, China's climate goals for 2050 are gravely jeopardized if the industry's enormous ecological impact is not addressed. This research develops a conceptual framework for environment-security-economic evaluation of the high-carbon energy sector. Moreover, it constructs a system dynamics material model to predict the upgrade plan's industrial capacity and carbon emission scale. Finally, it calculates the dynamic optimal deployment scale at various epochs based on the sequential decomposition goal of "carbon peak" and "carbon neutrality." These measures are needed to anticipate the development of China's CTL industry. The road map predicts that by 2025, China's CTL industry will be able to meet at least 5% of the country's total oil demand. The adoption of scale A process will improve the economic and environmental performance of the CTL industry due to the cost savings and efficiency gains at a larger scale. In addition, the industry's overall energy efficiency will rise in 2023 and exceed 50% after 2035. Findings indicate that the CTL sector will approach a carbon peak in 2029 and neutrality and transition to hydrogen generation by 2050.

A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Exploring the path and synergistic effects of “green intelligence integration” for improving industrial carbon emission efficiency: comparative empirical study of China’s five major urban agglomerations

Exploring the path and synergistic effects of “green intelligence integration” for improving industrial carbon emission efficiency: comparative empirical study of China’s five major urban agglomerations

The effect of the digital economy on carbon emissions in China's construction industry: evidence from spatial econometric analysis.

Carbon emission reduction in the construction industry is vital for realizing sustainable development, and the development of the digital economy plays an important role in this process. The impact of the digital economy on reducing carbon emissions in the construction industry is empirically explored through econometric analyses on a sample of panel data from 30 provinces in China from 2012 to 2021. The empirical results show that developing a digital economy can significantly reduce the construction industry's carbon emission intensity. Additionally, this impact has a significant spatial spillover effect and can benefit the neighboring regions. The mechanism test shows that the digital economy can reduce carbon emissions by improving the technological level of the construction industry. Moreover, the inhibiting effects of the digital economy on carbon emissions in the construction industry vary across different regions. They are more pronounced in the eastern and western regions of low coal-consuming regions. These findings offer valuable insights for policymakers to help drive the deeper integration of the digital economy with the construction industry and facilitate its transition to low-carbon development.

Influencing Factors and Emission Reduction Paths of Industrial Carbon Emissions Under Target of “Carbon Peaking”: Evidence from China

Influencing Factors and Emission Reduction Paths of Industrial Carbon Emissions Under Target of “Carbon Peaking”: Evidence from China

Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.

Index System of Low-carbon Driving Factors of Construction Industry in Hebei Province

Under the background of "double carbon" goal, the low-carbon development of construction industry in Hebei Province has become an important part of achieving the carbon peak in 2030 and carbon neutrality in 2060. The construction industry is one of the main sources of carbon emission in Hebei Province, and how to promote its low-carbon development is an urgent issue to be solved. Based on the five aspects of technology, energy, society, economy and industry, this paper constructs the low-carbon driving factor index system of the construction industry. The research shows that technological innovation, energy structure optimization and economic structure adjustment are the key factors to promote the low-carbon construction industry. In addition, by analyzing the current situation and future trend of carbon emissions of the construction industry, this paper puts forward corresponding policy suggestions, which provides theoretical support for the government to formulate countermeasures for the low-carbon development of the construction industry. The conclusion points out that the research and development and application of low-carbon technology, the optimization of energy structure and the green transformation of industrial structure will be the core path to realize the low-carbon development of the construction industry.

Carbon trading pilot and low-carbon development of China’s textile industry

To explore whether a carbon trading pilot (CTP) can promote the low-carbon development of China's textile industry (CTI), this paper used a difference-in-difference model to study the impact of CTP on the carbon emissions in CTI and further analysed its regional heterogeneity. The research results showed that the impact coefficient of CTP on the carbon emissions in CTI was significantly negative, indicating that CTP could reduce the carbon emissions in CTI. Robustness tests such as the Placebo test, replacing the explained variable and the estimation method and excluding the interference of other policies verified the robustness of the research results. The dynamic regression results revealed the carbon reduction effect of CTP became stronger as the time of its implementation increased. At the regional level, CTP could also significantly lower the carbon emissions in the textile industry both in the eastern and western regions, and the reduction effect in the western region was greater than that in the eastern.

Examining the Causal and Heterogeneous Influence of Three-Dimensional Urban Forms on CO2 Emissions in 285 Chinese Cities

Despite the efforts to examine the influence of urban forms on CO2 emissions, most studies have mainly measured urban forms from a two-dimensional perspective, with relatively little attention given to three-dimensional urban forms and their causal relationships. Utilizing the built-up area dataset from the Global Human Settlement Layer (GHSL) project and the carbon emission dataset from the China City Greenhouse Gas Working Group (CCG), we examine a causal and heterogeneous effect of three-dimensional urban forms on CO2 emissions—specifically urban height, density, and intensity—in 285 Chinese cities. The empirical results reveal a robust and positive causal effect of 3D urban forms on carbon emissions. Even when incorporating the spatial spillover effect, the positive effect of 3D urban forms remains. Moreover, GDP per capita and total population have a greater impact on urban CO2 emissions. Additionally, we find that the influence of 3D urban forms on CO2 emissions is U-shaped, with total population serving as a moderating factor in this effect. Importantly, there is significant geographic and sectoral heterogeneity in the influence of 3D urban forms on CO2 emissions. Specifically, the influence of 3D urban forms is greater in eastern cities than in non-eastern cities. Furthermore, 3D urban forms primarily influence household carbon emissions rather than industrial and transportation carbon emissions. Therefore, in response to the growing challenges of global climate change and environmental issues, urban governments should adopt various strategies to develop more rational three-dimensional urban forms to reduce CO2 emissions.

Cultural Industry Agglomeration and Carbon Emission Performance: Empirical Analysis Based on 276 Cities in China

This study investigated the influence of cultural industry agglomeration on the energy carbon emission performance (CEP). Based on panel data from 276 cities in China, we used the Super-SBM model to measure the CEP. We then used the Tobit regression model to calculate the influence coefficient of cultural industry agglomeration and eight control variables on the CEP and analyzed the complex effects of cultural industry agglomeration on the CEP. The results showed that there is the phenomenon of “diseconomies of agglomeration” in cultural industry agglomeration, which cannot improve the CEP. For each unit of cultural industry agglomeration increase, the CEP decreases by 0.055; however, this phenomenon is not linear. Further research showed that the effects of cultural industry agglomeration showed a trend from good to inferior in the order of east, central, and west and did not improve with time. Finally, we used the panel quantile regression model and found that as the CEP levels rise, the negative impact of cultural industry agglomeration improves. Our research results show that strengthening the technical level to promote the upgrading of the cultural industry is the best way to achieve sustainable development. Governments at all levels should pay attention to the emission reduction potential of cultural industry agglomeration under high CEP levels and strengthen the benign agglomeration of the cultural industry.

Research on the Carbon Emission Prediction and Reduction Strategies for the Civil Aviation Industry in China: A System Dynamics Approach

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.

Allocating quotas for industrial carbon emissions fairly and efficiently to achieve “peak carbon”: A case of the yellow river basin in China

The “peak carbon” target has triggered wide-ranging and profound systemic changes and transformations that have left the provinces along the basin facing the dual dilemma of implementing carbon reduction actions and socioeconomic development. The allocation of carbon emission quotas is a fundamental task that reflects the division of labour and collaboration within a river basin to achieve the “peak carbon” target. Therefore, the fair and efficient allocation of industrial carbon emission quotas is the key to the implementation of carbon emissions trading and the establishment of carbon offset mechanisms. In the study, based on the description of the spatial and temporal evolution of the industrial carbon footprint and its carrying capacity, the industrial carbon offset area and industrial carbon reimbursement area are divided. Moreover, the allocation method of industrial carbon emission quotas is proposed by accounting for the principles of fairness, efficiency, and sustainability, and is verified using the provinces along the Yellow River Basin in China as a case. The results show that the industrial carbon carrying capacity is “unbalanced”. The provinces with strong carrying capacity are in the middle and upper reaches, and provinces with weak carrying capacity are in the middle and lower reaches. Moreover, the industrial carbon offset in the basin as a whole is higher than the industrial carbon compensation, which is potentially conducive to the implementation of cross-basin carbon offset. The weights of the equity, efficiency and sustainability indicators are 0.46, 0.23 and 0.31, respectively. The two industrial carbon compensated provinces can transfer the surplus industrial carbon emission quotas to the two industrial carbon offset provinces after obtaining the corresponding financial compensation. The Gini coefficient is 0.26, which is relatively fair. The study not only reveals the mechanism of the relationship among industrial carbon carrying capacity, carbon emission quota and carbon offset, but also provides a scientific basis for the establishment of a carbon emission quota trading market and the strengthening of the inter-provincial coordinated emission reduction incentives in the basin.

Influencing factors and predictions of carbon emissions for the chemical industry in China

As global warming increases the frequent occurrences of natural disasters, the reduction of carbon emissions has become an important issue around the world. The chemical industry is an important source of carbon emissions in China. The carbon emissions of the chemical industry are calculated from 2000 to 2019 by using the emission factor method. The logarithmic mean divisia index (LMDI) method is exploited to analyze the factors that influence carbon emissions, and the emissions variations are attributed to the contributions of carbon intensity, energy structure, energy intensity, industrial value-added rate, per capita industrial output value, and industrial scale. The results of decomposition show that per capita industrial output value is the main driving factor, and energy intensity is the main inhibiting factor of the chemical industry’s carbon emissions. In order to quantify the variation of carbon emissions, the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model is constructed and examined. Using the STIRPAT model, the basic scenario and energy intensity control scenario are set, and the carbon emissions are predicted, which shows that under a strict energy intensity control scenario, carbon emissions may reach a peak around 2031. The factors influencing the decomposition and prediction of carbon emissions should be helpful in reducing the carbon emissions of the chemical industry in China.

Does industrial agglomeration curb corporate carbon emissions? A perspective of financial constraints

This paper investigates the impact of industrial agglomeration on corporate carbon emissions in China. We measure the extent of industrial agglomeration utilizing distance-based continuous spatial measurement, differentiating between the quantity and scale of agglomeration. Using a panel of 7895 firm-year observations from 2007 to 2019, our findings indicate that regional industrial agglomeration contributes to a reduction in carbon emissions for firms located within those regions. Specifically, this reduction is primarily achieved through the alleviation of corporate financial constraints. We demonstrate that industrial agglomeration facilitates firms in accessing informal finance (e.g., trade credit) more effectively than formal finance (e.g., bank loans). Additionally, we find that the effect of industrial agglomeration on reducing carbon emissions is more pronounced for firms with higher environmental responsibility, while it is diminished in regions with greater marketization. The carbon reduction effect is also more significant for heavily polluting firms and those situated in areas with stricter environmental regulations. Furthermore, our study reveals that the implementation of green credit policies may weaken the carbon reduction effect of agglomeration by limiting access to financial resources. The robustness of our main results is confirmed through the Bartik-IV test and other robustness checks. Taken together, our findings suggest that industrial agglomeration can effectively lower corporate carbon emissions by easing financial constraints, offering valuable insights into strategies for enhancing corporate environmental performance.

Would carbon tax policy promote real estate prices?

Real estate plays a pivotal role in carbon emissions. Carbon tax policy is a crucial measure for mitigating enterprises' carbon emissions. However, imposing a carbon tax on productive enterprises in the real estate industry to reduce emissions may increase their production costs, which could easily be passed on to buyers, thus inflating real estate prices and impeding policy effectiveness. To examine the impact and negative consequences of carbon tax policy applied to the real estate industry, we develop a dynamic stochastic general equilibrium model encompassing building materials manufacturers, real estate firms, and the environmental regulatory government. Findings indicate that implementing the carbon tax policy significantly reduces carbon emissions in the real estate industry. However, the existence of a price transmission mechanism makes carbon tax policies susceptible to driving up real estate prices. Further welfare analysis shows that increasing carbon tax rates will result in social welfare losses, allowing the government to modify and refine its policy objectives.

Exploring the nexus of oil price shocks: Impacts on financial dynamics and carbon emissions in the crude oil industry

This empirical study delves into the intricate interplay between oil price shocks (OPS) and their dual impacts on both carbon emissions and the financial performance of the crude oil industry. Utilizing a sophisticated econometric approach, the study employs a Nonlinear ARDL model to meticulously scrutinize the enduring and immediate associations between oil prices and key financial indicators such as stock prices, dividends, and profit ratios of oil exploration industries. Given the significant fiscal and ecological transformations in China during this period, the chosen factors provide a robust framework for measuring changes at both microeconomic and macroeconomic levels, contributing valuable insights to sustainable economic development strategies. Our findings elucidate a statistically significant influence of OPS on the financial performance of the oil industry, particularly in the short term. The increase in the volatility level of OPS manifests a discernible decline in stock prices and profit ratios. Intriguingly, the study further uncovers a nuanced facet of OPS, revealing a prolonged and beneficial impact on carbon emissions in the long term. More importantly, heightened oil prices serve as a catalyst, motivating oil exploration companies to intensify production and exploration activities, thereby increasing carbon emissions in a direct relationship. The implications of these findings are pivotal for policy making, underscoring the imperative for decision-makers and industry stakeholders to pivot towards cleaner, more sustainable energy sources. The research augments the ongoing discourse surrounding the intricate nexus between energy costs and carbon emissions, furnishing invaluable insights that could steer the development of policies geared towards fostering sustainability and mitigating the adverse effects of climate change.