Emission Reduction Goals Research Articles

Enhanced bi-layer scheduling strategies for the cascade hydropower-photovoltaic complementary system using a novel meta-heuristic algorithm

Improving energy efficiency is crucial for China’s power industry to meet global energy conservation and emission reduction goals. The rapid development of photovoltaic (PV) and hydropower has greatly assisted in the construction of China’s novel power system. The stochastic characteristics of PV power generation pose significant challenges to the reliable and economical scheduling of power systems. In fact, the cascade hydropower station can effectively address the issue. To fully utilize the advantages of hydropower, this paper proposes a bi-layer scheduling optimization model for the cascade hydro-PV complementary system considering power market. The upper-layer model simultaneously maximizes the benefit and minimizes the output volatility of the complementary system. The lower-layer model carries out market clearing with the objective of social cost. Besides, PV uncertainty and market price volatility are considered in the decision-making process for power market transactions. To solve the bi-layer model, a novel meta-heuristic algorithm (geometric mean optimizer) is applied, demonstrating excellent performance compared to similar methods. For the complementary system, the results show that its total power output can be improved, and its output volatility can be effectively alleviated.

Does the Energy-Consumption Permit Trading Scheme Improve Carbon Emission Performance? Evidence from a Quasi-Natural Experiment in China

The Chinese government launched the Energy-Consumption Permit Trading Scheme (ECPT) in 2016 as a pilot policy initiative to achieve energy conservation and emissions reduction goals. To generate a quasi-natural experiment, this study used a difference-in-differences (DID) approach with panel data from 2006 to 2020 in China. The goal was to explore the impact of the ECPT policy on the carbon emission performance (CEP) as well as analyze the underlying influence processes. The findings indicate that applying the ECPT has a considerable positive influence on the CEP. In comparison to provinces that did not engage in the pilot program, those that did reported a 4.5% improvement in CEP. We can safely declare that the results remain consistent and trustworthy after undergoing a battery of rigorous tests to assess the robustness of our findings. The mechanism’s evaluation revealed that the ECPT has the potential to improve the CEP, mostly via changing energy consumption patterns and stimulating technological innovation. Furthermore, when we considered heterogeneity, we discovered that factors such as geographical location, the level of green financing development, and the level of environmental regulation may affect the ECPT policy effects.

AI-orientation and company climate action: The moderating role of dependency structure and innovation capability

As climate change risks increasingly threaten corporate economic activities, an increasing number of firms become aware of need to take actions and develop strategies for addressing climate change or sustainability. However, B2B companies may face challenges in implementing green strategies because of their close relationships with other partners in the supply chain. Hence, from a strategic orientation perspective, this study explores the impact of AI orientation on corporate climate actions based on the dynamic capabilities view. Using a sample of 4050 China's listed companies from 2018 to 2021, the study employs instrumental variables to examine the impact of AI orientation on corporate climate actions, while also considering the moderating effects of dependent structure and innovation capability. The findings suggest that AI orientation promotes corporate climate actions, and that higher innovation capability reinforces this effect. However, the stronger the external dependency is, the weaker the positive effect of AI orientation on corporate climate action will be. The research outcomes provide theoretical and empirical evidence for B2B firms to reasonably apply AI to achieve goals of environmental protection and emission reduction.

Factorial optimization-driven input-output analysis for socio-economic and environmental effects of GHG emission reduction in electric power systems – A Canadian case study

Low-carbon transition of electric power system plays an important role in meeting national greenhouse gas (GHG) emission reduction goals. Analyzing the effects of such transition on various sectors could provide targeted and effective mitigation policy recommendations at sectoral level. In this study, a factorial optimization-driven input-output model has been developed to explore socio-economic and environmental (SEE) effects of GHG emission reduction in Canada's electric power system under uncertainty and their interactions. The results highlight the importance of optimizing the structure of a certain system (e.g., energy system or electric power system) on the emission reduction of the whole society under a specific mitigation target. Significance of indirect GHG emissions for sectoral emission reduction policy formulation is further emphasized, especially for agriculture- and manufacturing-related sectors. In addition, factors with significant interactive effects on sectoral total outputs have been identified. Increasing the proportion of clean power (i.e., wind/solar power, small modular reactor power, and coal-fired power with carbon capture and storage technology) is conducive to promoting sectoral total outputs and GHG emission reduction by 2050. The modelling framework can be extended and applied to other regions to help analyze SEE effects under various emission mitigation policies and scenarios.

A long-term impact assessment of carbon capture (storage) investment conducted by conventional power company on sustainable development

Carbon capture and storage (CCS) and renewable energy constitute two primary pathways towards achieving global emission reduction goals. In comparison to the fervor for renewable energy investment, the focus on CCS seems to be underwhelming, especially for conventional power companies in a transition phase. This study focuses on evaluating the long-term feasibility of integrating CCS technology into the strategic planning of the conventional power company. To evaluate the environmental and economic impacts of CCS under the context of carbon trading mechanism and renewable energy, a system dynamics model was employed. The principal findings of the study underscore that although the implementation of CCS may augment the financial burden on conventional power companies, this strategic investment preserves the potential to achieve carbon neutrality by 2060. It emphasizes that relying solely on renewable energy would make it difficult to realize this emissions commitment. Conventional power companies should earnestly contemplate the adoption of CCS technology as a strategic investment avenue to accomplish their carbon neutrality objectives. It is also suggested that subsidies and moderate policy intensity within the carbon trading mechanism can serve as effective incentives for conventional power companies to invest in and develop CCS technology. Policymakers, investors, and business owners can leverage these findings as decision-support tools in devising comprehensive strategies that encompass both CCS and renewable energy initiatives.

Does Carbon Trading Lead to Green Technology Innovation: Recent Evidence From Chinese Companies in Resource-Based Industries

The carbon emission trading scheme (ETS) is critical to China's ability to reach the established carbon dioxide (CO₂) emissions reduction goal by 2030 and carbon neutrality by 2060. To determine whether the pilot ETS policy promulgated in China in 2011 has induced green technological innovation, we conducted a multidimensional empirical test to assess the financial and green patent licensing data of Chinese A-share (publicly listed) enterprises in Shanghai and Shenzhen from 2004 to 2019. Using a difference-in-difference model with robustness checks, we did not find that the pilot ETS has had green innovation effects on entire industries or all manufacturing. However, we did identify significant green innovation improvements in some resource-based industrial enterprises, and the promotion effect led to more green utility models than green invention patents. Furthermore, the policy's greatest green-innovation effects are reflected primarily in new patents/utility models for low-carbon, energy-saving, and alternative energy technologies, particularly among large and nonstate enterprises. The impact mechanism tests indicate that the pilot policy encourages green innovation primarily through four policy instruments: increasing benefit income for resource-based industrial enterprises, reducing financial constraints, granting subsidies, and increasing incentives for enterprise R&D. Therefore, this article not only confirms the effectiveness of carbon emission trading market policy, but also provides scientific guidance for the realization of green technology innovation.

Research on the motivation, mode and performance of green transformation of textile companies

The characteristics of high pollution and high energy consumption of the traditional textile industry make it urgent for a comprehensive optimization and upgrading after the goal of “carbon peak and carbon neutral” in China. Based on the research background of “double carbon” target, this paper studies the transformation of the textile enterprises, choose has the country’s largest textile industry cluster of Zhejiang Shaoxing KeQiao for research object, through the questionnaire survey, field research, case study method found that: industry transformation effect overall positive momentum, but there are still competitiveness, power, the target. Furthermore, this paper selects typical enterprises and divides different forms of green transformation into substantive green transformation, arbitrage green transformation and stress green transformation. Moreover, it summarizes the main characteristics of transformation mode to create precise “5G”, innovate low energy consumption technology and promote green finance. Finally, the performance results of the three transformation modes are analyzed, which expands the research direction of the transformation mode for the green goal of energy conservation and emission reduction in the textile industry, points out a new road and provides new enlightenment for the exploration of the green transformation mode of enterprises. Green manufacturing is the decisive factor and the only way to realize the social sustainable development strategy, and is the inevitable trend of the development of manufacturing industry.

Evaluation and Comparison of Research Methods on Driving Factors of Carbon Emission in Industrial Parks

As the primary territories of regional carbon emission and energy consumption, most industrial parks only simply stack technology and policy under the urgent goal of emission reduction and carbon reduction. As a result, certain measures may even have negative impacts. As the primary means to determine the characteristics of carbon emissions in industrial parks, studying the driving factors of carbon emissions and proposing targeted measures can effectively achieve the carbon emission reduction goals. However the mechanism analysis of driving factors of carbon emission in industrial parks is the important means to study the characteristics of carbon emission. The selection of driving factors is many and complicated, and the selection of mechanism analysis methods is different, so it is difficult to have a comparison of research results Based on the quantitative relationship model between carbon emissions and driving factors and the method of action mechanism analysis in industrial parks, this paper evaluates and compares the relevant studies, summarizes their advantages and disadvantages, and makes relevant comments and prospects. For providing reference for the practice of driving factors of carbon emission in industrial parks, and put forward the basis for the research of carbon emission prediction.

双碳背景下绿色金融对农业碳排放的影响研究

Under the deepening of economic globalization and the “dual-carbon”policy, China has paid more attention to the construction of an ecological civilization and has gradually increased its efforts in the green development of agriculture, which has initially gained good development results. However, in agricultural development, in the ecological environment management, still face greater challenges, should use more ways to better achieve the goal of green development of agriculture. For green finance, it belongs to a financial tool and policy instruments, can effectively enhance the efficiency of agricultural production, and play an important role in the control of agricultural carbon emissions. Based on this, this paper briefly discusses the green finance and agricultural carbon emission related content, analyzes the status quo of green finance and agricultural carbon emission, explains the impact of green finance on agricultural carbon emission, and puts forward the relevant policy recommendations of green finance to reduce agricultural carbon emission in the context of dual-carbon, with a view to providing assistance for the realization of the goal of agricultural carbon emission reduction.

How does firm digitalisation drive management activities to achieve low-carbon development?

ABSTRACT Employing digital technology to achieve carbon emission reduction is a promising approach. This study combines two methods, including a systematic literature review (SLR) and empirical analysis to study the multiple paths of firm digitalisation to achieve carbon emission reduction through management activities. First, we employ an SLR method to identify the applications of digital technology for carbon emission reduction practice and the multiple paths of carbon emission reduction through management activities. We then empirically test the relationship between firm digitalisation, management activities and carbon emission reduction. The relevant findings are twofold. (1) The SLR reveals that artificial intelligence is the most effective technology to achieve carbon emission reduction, followed by the Internet of Things, big data analytics and cloud computing. These technologies often collaborate synergistically to achieve carbon emission reduction goals. (2) The SLR and empirical analysis reveal that firm digitalisation achieves carbon emission reduction through three management activities, including production operation management such as energy forecasts, resource control and preventive maintenance; innovation management such as process, energy and technological innovation; and logistics and supply chain management such as packaging management.

Multi-objective optimisation model of a low-cost path to peaking carbon dioxide emissions and carbon neutrality in China

A low-cost path system for achieving carbon neutrality in China was modelled using multi-objective programming by integrating industrial production, electric power, heating, transportation, and forest carbon sequestration. We aimed to minimise the total system cost, CO2 emissions, and air pollutants. The constraints included China's targets of peaking CO2 emissions before 2030; achieving carbon neutrality before 2060; ensuring industry, power, heating, and transportation supplies; promoting green energy; and implementing emission control. The model accounted for industries with high coal consumption, such as steel and chemical industries. Various power sources were considered, including coal-fired, nuclear, wind, and solar energy. Forest carbon sink and carbon capture and storage technologies were employed to achieve the emission reduction goals. The model, which was validated using available research data, offered cost-effective path schemes and exhibited high validity. Our findings emphasise the importance of structural adjustments and emission control, with electric power, heating, and transportation sectors showing higher feasibility and providing greater contributions to achieving carbon neutrality than other industries. Conversely, industrial transformation in sectors such as iron and steel, chemical, and construction materials had low feasibility and limited contribution. The modelling outcomes provide valuable insights for developing low-cost, carbon emission-targeted transportation structures in China's complex system. The results presented here demonstrate the global applicability of this method in contributing to plans aimed at meeting key carbon reduction targets.

Challenges to aviation in the global warming context

The aviation sector has undergone substantial growth, leading to a corresponding increase in emissions attributed to this industry. This escalation in emissions has notably contributed to the intensification of global warming. This research provides a comprehensive examination of various prospective solutions to mitigate these emissions. Among the proposed solutions is the adoption of an intercooled turbofan engine. This specific engine type holds the promise of realizing a 3.1% reduction in fuel consumption compared to traditional engines. Furthermore, the incorporation of a recuperator in an intercooled turbofan engine could potentially lead to a significant decrease in fuel consumption by up to 30%, alongside a remarkable 20% improvement in engine efficiency. The discourse also extends to the subject of hydrogen-powered aviation, underscoring its substantial potential in achieving emissions reduction goals. However, the associated challenges, especially those related to hydrogen storage, are also explored in this study. The forthcoming era of jet engine technology may witness the amalgamation of intercooled turbofan engines and hydrogen energy sources as a viable pathway for substantial emissions reduction.

Carbon constraints and carbon emission reduction: An evolutionary game model within the energy-intensive sector

Energy-intensive enterprises form the backbone of low-carbon transformation in our society, which is attracting significant attention in terms of government policy-making. However, existing studies focus largely on the behavior of governments or enterprises in emission reduction; what is not asked—and therefore not explained—is the coordination mechanism between government and industry. More crucially, very few studies have taken the initial willingness of both governments and enterprises into consideration. The absence of that research may lead to a certain amount of conflict between economic development and environmental protection by local governments, leading to looser regulation. Meanwhile, energy-intensive enterprises would perceive local governments as tending toward laxity, further negatively affecting emission reduction. To help fill in these gaps in the literature, we build an evolutionary game model consisting of energy-intensive enterprises and the government. Specifically, we first construct a revenue matrix including governments and energy-intensive enterprises, and then analyze their evolutionary stabilization strategies in four cases: strict regulation with carbon emissions, strict regulation without carbon emissions, loose regulation with carbon emissions, and loose regulation without carbon emissions. It should be noted that the model is built with consideration of penalties levied by higher-level governments for lax regulation by local governments. Likewise, in the initial game state, the probabilities of energy-intensive enterprises taking the initiative to reduce emissions and strict government regulation represent the initial intention the respective parties. Finally, the dynamic evolution process is simulated numerically. Our main findings are as follows. 1) Government regulations and costs of reducing emissions are determinants of the equilibrium state in the game model. In the case of loose regulations or high emission reduction costs, both governments and enterprises embrace strategies with a negative effect on emission reduction. 2) Without effective external constraint mechanisms, both the government and enterprises would make decisions to maximize their own benefits. Doing so would lead to a sub-optimal equilibrium, impeding the achievement of ideal emission reduction goals. 3) The initial willingness of governments and enterprises determines emission-reduction paths. Likewise, the initial intention to engage in strict regulation by governments exerts a stronger constraint on enterprises. Therefore, to guarantee policy efficiency, governments need to implement strict regulations, decrease enterprises’ emission reduction costs, and enhance the initial willingness of both sides. Under these conditions, energy-intensive enterprises would be more likely to accomplish low-carbon transformation.

Incentive-oriented power‑carbon emissions trading-tradable green certificate integrated market mechanisms using multi-agent deep reinforcement learning

Due to the stricter emission reduction and renewable energy consumption goals in China, the carbon emissions trading (CET) market and tradable green certificate (TGC) market, as key means to achieve these goals, urgently need to adjust the current operating mode timely and maximize market effectiveness by introducing adaptive incentive mechanisms. Therefore, taking the power industry as an example, an incentive-oriented power-CET-TGC integrated market simulation framework based on multi-agent deep reinforcement learning is constructed. The impact of consignment auction mechanism, traditional auction mechanism, and voluntary TGC trading mechanism on the transaction situation of each market is prospectively analyzed. The results demonstrate that with the introduction of various incentive mechanisms, the transaction scale and prices of CET and TGC markets increase, and the effectiveness of carbon reduction and renewable energy consumption is significant. Among them, auction mechanisms are valid way to promote the explicit cost of carbon emissions, and the voluntary TGC trading mechanism is an important means to enhance the clean value of green energy. In addition, it is recommended to adopt consignment auction mechanism under the short-term goal “carbon peak” of pursuing economic benefits and stable emission reduction, while traditional auction mechanisms are recommended under the long-term goal of “carbon neutrality” of urgently requiring large-scale emission reduction. Moreover, the voluntary TGC trading mechanism can achieve synergy and connection between the TGC market and the green electricity market to a certain extent. These insights can provide mechanism reference for the sustainable development and construction of CET and TGC markets.

Option Pricing Practices Based on the B-S-M Model in Carbon Markets

Carbon trading refers to the buying and selling of carbon emission allowances, stimulating businesses to adopt low-carbon development strategies through price signals. By allocating limited emission allowances to emitters, the carbon market encourages the reduction of carbon emissions to achieve emission reduction targets. Market participants can compensate for emissions exceeding their own quotas by trading carbon emission allowances, thus minimizing the cost of emission reductions. The Chinese carbon market has a large trading volume and numerous development opportunities, but currently, the carbon financial market is not fully open. In comparison to Western countries, China lacks carbon emission derivatives such as futures, options, and swap contracts. The risk and return of carbon emission allowance options are estimated and managed using the B-S-M option pricing model. This paper utilizes data from the Shenzhen Carbon Emission Allowance Exchange as a sample to conduct option pricing practices for carbon emission allowances using the B-S-M model. The aim is to promote research and development in the carbon emission allowance market and contribute to the achievement of emission reduction goals.

The energy-saving effect of industrial chain synergistic division: Evidence from China's industrial chain

Under the influence of global trade policies, countries gradually build their entire industrial chains, and industrial policies show a localized development trend. In this context, what impact will the industrial chain localization policy have on the low-carbon transition and high energy efficiency? It is significant for achieving the energy conservation and emission reduction goal. Therefore, this paper uses non-competitive input-output tables from 2002 to 2017 to construct China's domestic industrial chain panel data. We combined the econometric model to explore the influence of industrial chain synergistic division on energy intensity. The research found that the industrial chain synergistic division effect can significantly reduce energy intensity. The results were robust after some endogeneity tests. Secondly, the energy-saving effect brought by the synergistic division is more obvious in high-energy dependence and high-stage industrial chains. There is still room to improve the production efficiency brought by the division network effect. Finally, the industrial chain synergistic division can reduce energy intensity by improving labor productivity, enterprise productivity, and enterprise competition mechanism. This study can provide targeted policy recommendations for China to achieve structural energy-saving. It can also provide a reference for other countries to achieve carbon neutrality.

Land-use carbon emissions and built environment characteristics: A city-level quantitative analysis in emerging economies

Rapid urbanization and subsequent urban land expansions have led to land use changes, emerging as a prominent source of anthropogenic carbon emissions in the developing world. While numerous studies have quantified land-use carbon emissions (LUCE), there is still a lack of rigorous statistical analyses examining the driving factors of LUCE, particularly in the post-2015 era, when stringent environmental pollution regulations and carbon emission reduction policies have been implemented worldwide. This study aims to assess city-level LUCE in emerging economies and to further investigate the drivers of inter-city variations in LUCE through an extended STIRPAT model, taking Guangdong Province in China as an example. The results indicate a substantial increase in LUCE from 2000 to 2020, reflecting a surge in energy consumption associated with land-use transitions from natural land to construction land. The growing disparity between direct and indirect carbon emissions underscores the increasing significance of construction land planning and management in achieving emission reduction goals. Furthermore, our findings from the extended STIRPAT model shed light on carbon neutrality pathways by revealing the potential for LUCE reduction through adopting sustainable built environment planning.

A Review of Research on the Impact Mechanisms of Green Development in the Transportation Industry

Green development in the transportation industry is a new type of development. As the huge energy consumption and carbon emissions generated by the transportation industry have caused many environmental problems, the healthy and environmentally friendly mode of industrial development has received more and more attention. However, the quantification of green development in the transportation industry varies in terms of boundaries, scope, and methods. Due to digital empowerment, the degree of influence and direction of the factors affecting the green development are not fixed. The prediction of future development prospects is relatively single-minded, lacking a comprehensive simulation scenario setting from multiple perspectives. This paper systematically reviews the research progress of green development of the transportation industry from three aspects: development performance assessment, influence mechanism analysis, and development path exploration. After a critical analysis, this study concludes that (1) a clear methodology is needed to assess the direct and indirect non-desired output results of the transportation industry; (2) considering the endogenization of the level of technology, the influence of the interaction between the influencing factors, etc., on the degree and direction of the role of the factors, a more scientific econometric model should be established for in-depth discussion; (3) resident travel options are an important factor affecting environmental issues in transportation. Carbon emission projections and analyses of emission reduction scenarios should integrate the multiple possibilities of residential preferences and policy incentives. The findings of this paper provide valuable references to the energy saving and emission reduction goals of the transportation industry, and the coordinated development of the industry and the economy.

Risk and self-optimization analysis of the carbon tax policy based on scenario simulation

The double dividend of the carbon tax policy has been a controversial topic. To comprehensively evaluate the benefits and risks brought by the carbon tax policy and contribute to China’s emission reduction goals, this paper establishes a carbon tax policy cycle simulation model based on China’s economic and energy data from 2010 to 2020 to explore the winner-curse phenomenons of the policy. To alleviate the winner’s curse of the carbon tax policy, this paper introduces a consumer behavior model to explore the optimization degree of loss aversion effect on the carbon tax policy. The research results show that the carbon tax policy has three kinds of winner’s curse phenomenons, namely, the improvement of environmental quality and the reduction of market capital, the decline of national carbon intensity and the increase of carbon intensity of three major industries, and the reuse of the tax revenue and the increase of economic loss. The loss aversion of consumers can alleviate the negative effect of the carbon tax policy and strengthen the positive effect. In addition, during the implementation of the carbon tax policy, the loss aversion effect can also reduce the polluted population by about 2%. Finally, based on the research results, the paper puts forward some feasible policy suggestions.

Research on Risk Measurement of China’s Carbon Trading Market

In today’s environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China’s carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China’s carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China’s carbon market.