Carbon Reduction Policies Research Articles

A bi-level multi-objective optimization approach for carbon policy formulation towards food waste resource treatment from environmental, energy and economic perspectives

Excessive food waste contributes to the greenhouse effect. Although low-carbon technology has the potential to reduce carbon emissions and generate renewable energy, the high costs may deter the adoption. To promote low-carbon food waste treatment, local authorities could develop carbon reduction policies targeted at the food waste treatment industry, which could lead to a game between local authorities and food waste treatment enterprises. Hence, a bi-level multi-objective optimization model is put forward to balance the decisions of local authorities and those of food waste treatment enterprises. Enterprises can choose which carbon reduction policies to implement, and local authorities determine the intensity of specific policies. After testing this model on Shenzhen’s food waste treatment system, the model’s validity and feasibility were confirmed. It was discovered that by identifying suitable carbon reduction policies, the model can help enterprises achieve more than 50% carbon reduction. Moreover, different enterprises may choose different carbon reduction policies to implement. Optimal food waste disposal arrangements need flexible adjustments. The constructed model can serve as a decision-making tool for city managers to reduce carbon emissions in food waste treatment.

Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R2 of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

Evaluation of a Peer-To-Peer Smart Grid Using Digital Twins: A Case Study of a Remote European Island

Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads.

Differences in willingness to pay for various types of carbon-labeled products: A dynamic perspective

Carbon labeling (CL) is a carbon reduction policy with development potential that has not yet been widely promoted worldwide. This study explores the trend of consumers' willingness to pay (WTP) for different types of carbon-labeled products from a dynamic perspective, to provide a reference for future pilot projects. The research team collected 3788 valid questionnaires from China's first-tier cities spanning a five-year period and used interval regression to analyze the impact and change of consumer traits on WTP for the four types of carbon-labeled products in 2019 and 2024. The bootstrap method was further used to examine the impact mechanisms to explore ways to improve the current situation. This study found that (1) WTP for food, textiles, electronics, and other household products with CL generally decreased from 2019 to 2024. Textiles and electronics experienced a smaller decline, while food and other household products witnessed larger decreases. (2) The effect of consumer characteristics on WTP changes from 2019 to 2024. Consumers' frequency of low-carbon behaviors no longer significantly affects WTP across all product categories, while attitudes toward CL only affect textiles and electronics. (3) Consumers' perceived environmental value is the mechanism by which personal traits affect WTP. Finally, based on the findings, this study suggests that the selection of textiles and electronics as a pilot product is more likely to be accepted by consumers and that emphasis should be placed on the promotion of the environmental values of CL.

Are air travelers willing to pay for mandatory carbon emission policies? Evidence from China

The aviation industry is one of the fastest-growing sectors in terms of CO2 emissions and also one of the most challenging to decarbonize. China has become the world's second-largest aviation market. While a nationwide mandatory carbon reduction policy has yet to be introduced in China's aviation sector, it may be implemented in the coming years. The implementation of mandatory carbon reduction policies will inevitably raise questions about the distribution of additional costs. Therefore, air travelers' willingness to pay (WTP) and the factors influencing it are critical considerations for policymakers and airlines. However, there is a lack of research on the WTP of Chinese air travelers under mandatory carbon reduction policies. No studies have specifically focused on the regional differences in WTP among air travelers in China. Moreover, existing research on air travelers' WTP for carbon offsets rarely captures the impact of rational, emotional, and social interaction factors. Therefore, this study investigates the WTP of 3424 air travelers across 31 provincial-level administrative regions in China within the context of a carbon tax policy. Using the Heckman two-step model, the study analyzes the impact of rational, emotional, and social interactions on air travelers' WTP. The results indicate that, after removing biased samples, the average WTP among Chinese air travelers is 311.04 CNY ($44.14) per ton of CO2. The highest WTP is observed in the East China region at 341.20 CNY ($48.42) per ton of CO2, while the lowest is in the Northeast region at 263.19 CNY ($37.35) per ton of CO2. Epistemic performance expectancy, social performance expectancy, function performance expectancy, and anticipated pride positively influence WTP. In contrast, effort expectancy negatively impacts WTP. Additionally, social interaction can moderate the effects of certain rational and emotional factors on WTP. It is noteworthy that although anticipated guilt does not directly influence WTP, its positive impact on WTP can be moderated through social interactions. The results of this study can assist governments in formulating effective carbon emission reduction policies for the aviation industry. Moreover, these findings can provide valuable insights for airlines to adjust pricing strategies under mandatory carbon reduction policies, thus holding practical significance.

The impact of the carbon reduction policy effectiveness on energy companies' ESG performance

As one of the largest sources of carbon emissions, energy companies are motivated to improve their environmental, social, and governance (ESG) performance to attract investment and meet sustainability goals under the constraints of carbon reduction policies. However, it is currently unclear whether and how carbon reduction policies can help energy companies improve their ESG performance. Therefore, this paper comprehensively examines the impact of carbon reduction policies on the ESG performance of energy companies. First, we use the content analysis method to construct a novel index as a proxy of policy influence based on policy text, i.e., the carbon reduction policy effectiveness (CRPE) index. Then, we empirically examine the impact of CRPE on the ESG performance of energy companies using the panel data of Chinese A-share listed energy companies from 2010 to 2022. The empirical study shows that the CRPE can significantly improve the ESG performance of energy companies and remains robust to robustness tests. Mechanism tests show that it is due to the increase in executives with environmental backgrounds and executive compensation. For the three indicators (E, S, and G) of ESG performance, CRPE promotes environmental and social performance but reduces governance performance. Notably, the effect of CRPE on the ESG performance of clean energy companies and state-owned enterprises (SOEs) is more significant. These findings enrich environmental economic theories and provide a reference for energy companies to promote ESG practices and the low-carbon transition, helping to accelerate the process of carbon neutrality.

How do e-government and green technology innovation affect carbon emissions: Evidence from resource-rich countries in the Shanghai Cooperation Organization

The global challenges related to climate change and carbon emissions are constantly escalating. It is important to explore the factors that affect carbon emissions in countries rich in natural resources. However, the nexus among natural resources, e-government, green technology innovation, and carbon emissions has not yet been demonstrated. Therefore, this study explores the impact of the above three dependent variables on carbon emissions using data from five resource-rich SCO countries spanning from 2000 to 2022. This study further controls the quality of the institution and GDP. Unlike existing studies, it introduces an innovative interaction term between e-government and green technology innovation. The cross-sectional enhanced distributed hysteresis (CS-ARDL) method was employed for the analysis. The findings indicate that natural resources and GDP positively contribute to carbon emissions, while institutional quality and the interaction between green technology innovation and e-government have a negative impact. In the short run, green technology innovation has a non-significant impact on carbon emissions, while e-government has a positive impact. However, in the long run, both green technology innovation and e-government have a negative impact on carbon emissions. These findings suggest that promoting the simultaneous development of e-government and green technology innovation can help reduce carbon emissions. The paper is of great significance for further promoting the construction of e-government. In addition, the paper is of great reference value for policymakers in launching carbon reduction policies.

Carbon emission characteristics and carbon reduction analysis of employee travel-taking a research institute as an example

This paper adopts the “baseline scenario method” to construct a comprehensive model for calculating and reducing carbon emissions generated by employee travel, including the accounting of carbon emissions from commuting and business travel, as well as the assessment of green travel for carbon reduction. The study employs methods such as questionnaires and on-site interviews to collect travel data from employees of a research institute in Beijing as a case study. The results show that employees’ commuting methods are diverse, with the subway being the primary mode of travel; however, business travel generates higher carbon emissions, particularly among employees with higher education levels. The research concludes that the model proposed in this paper provides a framework for preliminary carbon emission estimation, but to improve the accuracy of the estimates, more variables and factors need to be considered, and the limitations of the model are pointed out. The research findings have significant implications for policy and institutional practices, suggesting the adoption of more targeted measures to reduce the use of high-carbon-emission travel methods and to encourage the use of green travel options. With the continuous advancement of data collection technologies in the future, it will be possible to further establish a more refined carbon emission accounting model and obtain more accurate and comprehensive travel data, thereby providing solid data support for the development of more effective carbon reduction strategies and policies.

Interaction between product strategy selection and the carbon quota allocation policy

The interaction between the government’s carbon reduction policy and a firm’s product strategy has not been well studied in the literature. This paper considers the government’s two different carbon quota allocation policies for the cap-and-trade scheme and the firm’s two product strategies and investigates the interaction between them by establishing a theoretical model and deriving the optimal decisions. This paper first examines the firms’ selection of low-carbon products or ordinary product strategies under the government’s two carbon quota allocation policies and then studies the government’s optimal carbon quota allocation policy for overall social welfare, which is based on the firm’s two product strategies. Our analysis reveals that (i) when the government allocates carbon quotas aimed at reducing the firm’s total carbon emissions, the firm will choose the low-carbon product strategy. When the government allocates a carbon quota aimed at optimal total social welfare, the firm’s decision depends on the impact of total carbon emissions. (ii) To achieve optimal social welfare, the government will formulate different carbon quota allocation policies on the basis of firms’ different product strategies.

Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

Dynamic simulation of carbon emissions (CE) in megacities is crucial for regional carbon reduction management, however, limited simulation accuracy hinders its application in carbon reduction policies. An integrated modeling framework was developed based on high-resolution multi-source data to analyze the street-level carbon emissions in Wuhan from 2015 to 2030. First, we conducted principal components analysis on the 5 driving factors of carbon emissions (including point of interests, electricity consumption, gross domestic product, population, and nighttime light) to delineate single carbon emission character region (CECR). Then, a machine learning method was used to simulating CE and explaining the contributions of different CECRs. Multi-scenarios CE accountings also were conducted with CECR simulations and an improved cellular automata model. Results shows that: (1) CE in Wuhan showed strong aggregation during the historical period. The five CECRs formed a near-concentric circle. The changes of CECR reflected the enhancement of human activity. (2) CE gradually shifted from the central urban areas to the surrounding regions during the scenario period, showing significant spatial spillover effects. Administrative districts with lower CE density exhibited greater scenario variation in total carbon emissions, indicating a higher potential for carbon reductions. (3) The total CE of Wuhan (148.11 Mt) in 2030 is projected to increase by 42.6 % compared to 2015 in the baseline scenario, representing 105 % of the low scenario and 91 % of the high scenario. The growth rate of total CE in Wuhan significantly slows down (<1 %) under all scenarios. The high-resolution dynamic simulation of CE will provide an important scientific basis for low-carbon city management in China.

Fiscal decentralization's impact on carbon emissions and its interactions with environmental regulations, economic development, and industrialization: Evidence from 288 cities in China

Given the global focus on climate change and sustainable development, understanding the impact of China's fiscal decentralization on carbon emissions is essential for supporting green transformation and achieving carbon peaking and neutrality targets. This study examines data from 288 Chinese cities between 2003 and 2021, using fixed effects, moderation effects, and threshold effects models to assess the relationship between fiscal decentralization and carbon emissions, alongside its interaction with environmental regulation, economic development, industrialization, and city hierarchy. The results show that fiscal decentralization significantly aids carbon reduction, but stronger environmental regulations have not effectively slowed emission growth, creating a "green paradox." Relaxed regulations prioritize short-term economic gains, leading to underinvestment and fragmented regulatory efforts, which weaken emissions control. As economic development advances, fiscal decentralization's impact on carbon emissions shifts from positive to negative. Additionally, with increasing industrialization, the negative influence of fiscal decentralization on emission intensity becomes more pronounced. In non-first-tier cities, weak regulatory enforcement and limited autonomy hinder effective carbon reduction policies. To address these issues, the central government should enhance guidance and oversight of local environmental policies, ensure strict enforcement, optimize fiscal resources for environmental investment, and promote green economic transformation. A tailored approach to fiscal decentralization is needed to balance economic growth with environmental protection, achieving mutually beneficial outcomes.

A building operational carbon emissions database of 362 Chinese cities: Construction process and spatiotemporal characteristic analysis

The existing calculation methods of city building carbon emissions based on energy audits, city statistics and energy balance sheets have problems such as time-consuming and labour-intensive, missing data and large errors, resulting in the lack of a complete and accurate database of city building carbon emissions in China. To address this, the calculation method based on city statistical data (CSD-M) and the calculation method based on provincial energy balance sheets (PEB-M) were used to calculate the carbon emissions from city buildings. The PEB-M was corrected using CSD-M results, reducing the error to 12.8%. Based on CSD-M and modified PEB-M, a database of building carbon emissions in 362 Chinese cities was developed. Finally, the spatio-temporal characteristics of city building carbon emissions in China were analysed, and the current situation of carbon emissions in different cities was compared. This study provides a basis for future carbon reduction policies tailored to city characteristics.

New perspective, more rational decoupling: A case study of China

Scholars usually unconsciously employ the Tapio decoupling model with a static perspective and results-oriented philosophy, which often leads to errors. Therefore, we propose an improved Tapio decoupling model that adopts a dynamic perspective and process-oriented philosophy. Taking China, the world's largest carbon emitter, as a case study, we investigate the decoupling of its provincial industrial carbon emissions (ICE) from industrial value-added (IVA) during 2005–2020 using both the conventional and improved decoupling model, followed by a comparative analysis of their results. Our findings are as follows: (1) Both China's ICE and IVA exhibited a general upward trend during the study period, with non-linear annual ICE and IVA variations observed across all provinces. (2) Overall, China's provincial IVA increasingly decoupled from ICE, with some provinces achieving a strong decoupling state during 2015–2020; however, from the long-term perspective spanning the entire study period, most provinces remained in a weak decoupling state. (3) The conventional decoupling model tends to yield overly optimistic results in empirical study of China, with the decoupling indices during the periods of 2005–2010 and 2005–2020 determined by the conventional model even had significant statistical difference between those determined by the improved model. (4) To ensure equity, differentiated carbon reduction policies should be tailored to each province, considering factors such as absolute carbon emissions, short-term decoupling states, and long-term decoupling states. The improved Tapio decoupling model is proposed as a valuable framework for researchers engaged in related studies.

Coordinated planning of thermal power, wind power, and photovoltaic generator units considering capacity electricity price

AbstractWith the implementation of China's carbon reduction policies, the role of thermal power units will transition to a regulating power source. Hence, the electricity market fails to accurately reflect the capacity value of thermal power units, resulting in potential future losses for these units. Therefore, it is imperative to establish a rational capacity compensation mechanism that guarantees the revenue of thermal power units and offers effective investment and construction signals. Therefore, this paper proposes a capacity compensation mechanism for thermal power units based on effective capacity. To achieve this, a two‐layer power source planning model is established. At the upper level of the model, the installed capacity and capacity price of various types of power sources are optimized, while minimizing the operating costs of the planning horizon year under constraints such as annual net profit of units. The lower level focuses on the operation of typical days, optimizing the output of various types of units. Through case analysis, it can be concluded that the proposed model can achieve coordinated planning of capacity and capacity prices for various types of units, effectively ensuring the economic efficiency of the system while safeguarding the revenue of each unit.

Health equity and synergistic abatement strategies of carbon dioxide and air pollutant emissions reduction in China’s eastern coastal area

Abstract Quantifying regional health disparities linked to air pollution is essential for enhancing air quality and attaining carbon neutrality objectives. Nonetheless, the efficacy of proactive policies in ensuring equitable health protection in China’s Eastern Coastal Area (ECA) remains uncertain. Here, we employed an integrated assessment model to assess the combined reduction of carbon dioxide (CO2) and atmospheric pollutants and their health repercussions in the ECA of China. Our findings reveal that 273 000 premature deaths are attributable to air pollution in 2060 in the ECA in the absence of mitigation policies. Conversely, carbon reduction policies are poised to curtail 80% of CO2 emissions, alongside reductions of 76% for NOx, 79% for SO2, 80% for PM2.5, 72% for VOCs, and 66% for NH3 emissions. Air pollution control policies could mitigate premature deaths by 19 600, while carbon reduction policies could potentially lower them by 50 800. The health inequality coefficient among provinces stands at 0.19, primarily attributable to significantly higher mortality rates in Hebei and Shandong. These findings yield valuable insights for crafting synergistic abatement strategies in similarly imbalanced developmental regions grappling with comparable environmental challenges.

Research and development efficiency, state-owned enterprises, and carbon intensity in China

This study investigates the effects and transmission mechanisms of research and development (R & D) efficiency and the proportion of state-owned enterprises (SOEs) on carbon intensity using data from 30 Chinese provinces. The findings show that improving R & D efficiency can lower carbon intensity by promoting technological progress. However, more SOEs can increase carbon intensity by inhibiting technological progress. Thus, a higher proportion of SOEs can reduce the positive impact of R & D efficiency on decreasing carbon intensity. Additionally, the proportion of SOEs has a moderating effect that extends beyond provincial boundaries, resulting in spatial spillover due to strong interconnections between provinces. Heterogeneity analysis indicates that this moderating effect is particularly pronounced in the central and western regions, as well as in the electricity sector. This variation is due to differences in economic development levels and government priorities. Given the characteristics of China’s carbon intensity, policymakers should shift from a one size fits all carbon reduction policy to prioritizing enhancing R & D efficiency, boosting the innovation capabilities of SOEs, and considering spatial linkages and regional disparities.

Determinants of spatiotemporal changes of land use carbon emissions for counties in Shaanxi Province, China.

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units arecrucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals.

Synergistic dance of digital economy and green finance on carbon emissions: Insights from China

China has recently implemented a dual-carbon strategy to combat climate change and other environmental issues and is committed to modernizing it sustainably. This paper supports these goals and explores how the digital economy and green finance intersect and impact carbon emissions. Using panel data from 30 Chinese provinces over the period 2011–2021, this paper finds that the digital economy and green finance can together reduce carbon emissions, and conducts several robustness tests supporting this conclusion. A heterogeneity analysis shows that these synergistic effects are more important in regions with low levels of social consumption Meanwhile, in the spatial dimension, the synergistic effect of the local digital economy and green finance adversely impacts the level of carbon emissions in surrounding areas. The findings of this paper provide insights for policymakers in guiding capital flow and implementing carbon-reduction policies while fostering the growth of China’s digital economy and environmental sustainability.

Regional carbon inequality prediction in China based on shared socioeconomic pathways: A human well-being equity perspective

Carbon inequality is strongly related to economic development and human well-being (HWB) improvements. However, relatively little research has been undertaken to predict future interregional carbon inequality in China from an HWB equity perspective based on scenarios combining shared socioeconomic pathways (SSPs) and carbon reduction policies. The biproportional scaling method named after economist Richard Stone (RAS) was used to predict China's 2020 to 2050 multiregional input–output (MRIO) tables. Then, these MRIO tables were used to simulate future net carbon emissions (NCEs) and net human well-being (NWB) transfers under the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios. China's interregional carbon inequality was predicted under different scenarios to clarify the ideal path for mitigating carbon inequality. In the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios, the total carbon emissions (CEs) clearly decrease, whereas the total HWB clearly increases. Transfers of NCEs between regions increases, and transfers of NWB between regions decreases in each of the four scenarios. According to the mean regional environmental inequality (REI) value, China's interregional carbon inequality is relatively low under the SSP1–1.5 °C and SSP1-NEU scenarios and relatively high under the SSP2–2 °C and SSP5-BAU scenarios. Mitigating interregional carbon inequality is a long-term and arduous task that requires commitments from governments, businesses and society. These findings clarify the optimal path for China to reduce carbon inequality in the future and provide a theoretical basis for government agencies to rationally adjust the current development model. Furthermore, they provide a supportive reference to help other economies achieve equitable and sustainable development.

A tracking control method for electricity-carbon emission forecasting

This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.