Carbon Constraints Research Articles

Findings of Sudden Changes of Global Lithium Markets Subjected to Electric Vehicle Development, Lithium Exploration and Carbon Constraints: Based on Chaotic Dynamics Analysis

Findings of Sudden Changes of Global Lithium Markets Subjected to Electric Vehicle Development, Lithium Exploration and Carbon Constraints: Based on Chaotic Dynamics Analysis

A Linear Chance-Constrained Mixed-Integer Programming Model for Optimizing Regional Electric Power Systems under Carbon Constraints

In view of increasing population size and energy consumption, greenhouse gas (GHG) emissions are increasing and are one of the main causes of climate change. The regional electric power system is one of the main sources of carbon emissions, so there is an urgent need to optimize the regional electric power system to meet the Paris Agreement's long-term temperature goal. Therefore, this study provided a linear chance-constrained mixed-integer programming (LCMI) model with the objective of maximizing the total system profit and applying it to the regional electric power system. Chance-constrained programming and mixed-integer programming were integrated into the LCMI model to address input uncertainties. Including five commonly used power generation technologies, namely coal-fired, natural gas-fired, hydropower, wind power, and solar power, the model can provide the optimal electricity generation schemes and capacity expansion plans for different technologies at the regional level to meet the end-user’s needs while meeting the carbon dioxide emission targets under different risk levels. The outcomes of the research will offer decision-makers a framework for optimizing conventional regional electric power systems for their long-term sustainability in environmental and economic development.

Research on the Accurate Measurement and Analysis of Carbon Emissions by Carbon Real Measurement

The practice of carbon emission accurate measurement system application scenario of carbon electricity coupling and carbon real measurement is carried out, based on the carbon constraint background, the whole process of carbon production and emission is accurately pictured, and the carbon measurement real measurement and carbon electricity factor measurement model adapted to multiple industries is constructed in this way. The carbon measurement is carried out by collecting data from power plants in the field and automatically matching various carbon emission parameters with the calculation model to calculate the carbon emissions of the group in real time. Carbon electricity factor measurement for energy data that cannot be directly obtained, build an industry-wide carbon emission measurement and monitoring mechanism driven by electricity ties, and combine with third-party accounting agencies to certify the measurement results to indirectly display carbon emission data by electricity.

Synchronous coupling of defects and a heteroatom-doped carbon constraint layer on cobalt sulfides toward boosted oxide electrolysis activities for highly energy-efficient micro-zinc-air batteries.

The sluggish kinetics of oxygen electrocatalysis reactions on cathodes significantly suppresses the energy efficiency of zinc-air batteries (ZABs). Herein, by coupling in situ generated CoS nanoparticles rich in cobalt vacancies (VCo) with a dual-heteroatom-doped layered carbon framework, a hybrid Co-based catalyst (Co1-xS@N/S-C) is designed and synthesized from Co-MOF precursor. Experimental analyses, together with density functional theory (DFT)-based calculations, demonstrate that the facilitated ion diffusion enabled by the introduced VCo, together with the enhanced electron transport benefiting from the well-designed dual-heteroatom-doped laminated carbon framework, synergistically boost the bifunctional electrocatalytic activity of Co1-xS@N/S-C (ΔE = 0.76 V), which is much superior to that of CoS@N/S-C without VCo (ΔE = 0.89 V), CoS without VCo (ΔE = 1.23 V), and the dual-heteroatom-doped laminated carbon framework. As expected, the further assembled ZAB employing Co1-xS@N/S-C as the cathode electrocatalyst exhibits enhanced energy efficiency in terms of better cycling stability (510 cycles/170 hours) and a higher specific capacity (807 mA h g-1). Finally, a flexible/stretched solid state micro-ZAB (F/SmZAB) with Co1-xS@N/S-C as the cathode electrocatalyst and a wave-shaped GaIn-Ni-based liquid metal as the electronic circuit is further designed, which can display excellent electrical properties and long elongation. This work provides a new defect and structure coupling strategy for boosting the oxide electrolysis activities of Co-based catalysts. Furthermore, F/SmZAB represents a promising solution for a compatible micropower source in wearable microelectronics.

Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality

In the context of international carbon neutrality, energy prices are affected by several nonlinear and nonstationary factors, making it challenging for traditional forecasting models to predict energy prices effectively. The existing literature mainly uses linear models or a combination of multiple models to forecast energy prices. For the nonlinear relationship between variables and the mining of historical data information, the prediction strategy and accuracy of the existing literature need to be improved. Thus, this paper improves the prediction accuracy of energy prices by developing a “decomposition-reconstruction-integration” thinking strategy that affords medium- and short-term energy price prediction based on carbon constraint, eigenvalue transformation and deep learning neural networks. Considering 2011–2020 as the research period, the prices for traditional energy resources and polysilicon in clean photovoltaic energy raw materials are selected as representatives. Based on energy price decomposition using the Singular Spectrum Analysis (SSA) method, and combining it with Learning Vector Quantization (LVQ) cluster technology, the decomposed quantities are aggregated into price sequences with different characteristics. Additionally, the carbon intensity is considered the leading market’s overall constraint, which is input with the processed price data into a Long Short-Term Memory network (LSTM) model for training. Thus, the SSA-LSTM combined forecasting model is developed to predict the energy price under carbon neutrality. Four indices are employed to evaluate the prediction accuracy: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-squared. The results highlight the following observations. (1) Using a sequence decomposition clustering strategy significantly improves the model’s prediction accuracy. This strategy enhances predicting the overall trend of the price series and the changes in different periods. For coal price, the RMSE value decreased from 0.135 to 0.098, the MAE value decreased from 0.087 to 0.054, the MAPE value decreased from 0.072 to 0.064, and the R-squared value increased from 0.643 to 0.725. Regarding the polysilicon price, the RMSE value decreased from 0.121 to 0.096, the MAE value decreased from 0.068 to 0.064, the MAPE value decreased from 0.069 to 0.048, and the R-squared value increased from 0.718 to 0.764. (2) The prediction effect is better in the case of carbon constraint. Considering “carbon emission intensity” as the overall constraint of the leading market, it can effectively explore the typical characteristics of energy price information. Four evaluation indicators show that the accuracy of the model prediction can be improved by more than 3%. (3) When the proposed SSA-LSTM model is used to predict both prices, the results show that the evaluation index of the prediction error remained at about 1%, while the model’s accuracy was high. This also proves that the proposed model can predict traditional energy prices and new energy sources such as solar energy.

Advance selling of uncertain demand in low-carbon supply chain

PurposeThe development of low-carbon production is impeded by the investment costs of green technology research and development (R&D) and carbon emission reduction while facing the uncertain risk of emission reduction investment. With the government's carbon emission constraints, green manufacturers implement the advance selling strategy to increase both profit and reduction level. However, few studies consider the consumer's green preference and emission constraints in advance selling market and spot market independently. The authors' paper investigates the optimal strategies of advance selling pricing and reduction effort for green manufacturers to maximize profits.Design/methodology/approachThe authors' paper designs a stochastic model and investigates the manufacturer's optimal strategies of advance selling price and emission reduction efforts by categorizing different purchasing periods of low-carbon consumers. With the challenges of uncertain demand and government's emission constraints, the authors' develop the non-linear optimization model to investigate the manufacturer's profit-oriented decisions.FindingsThe results show the government's carbon constraints cannot influence the manufacturer's profit, but the consumer's low-carbon preference in the advance selling period can. Interestingly, the manufacturer will make fewer reduction efforts even when the consumers have stronger environmental awareness. In addition, the increasing consumer price sensitivity will exacerbate the profit loss from mandatory emissions reduction. Overall, for achieving a win–win situation between emission reduction and profit growth, green manufacturers should not only consider the sales strategies, market demand, and government constraints in a low-carbon market, but also pay attention to the uncertainty of green technology innovation.Originality/valueWith the consideration of the government's carbon emission constraints, uncertain demand, and low-carbon consumer's preferences, the authors' study innovatively incorporates the joint impacts of advance selling strategy and emission reduction effort strategy and then differentiates between two cases that pertain to the diverse carbon emission regulations.

Tech-eco Efficiency Evaluation of hydrogen production industry under carbon dioxide Emissions regulation in China

This study introduces the concept of non-separable variables, and conducts a super-efficiency slack-based measure (SBM) model considered undesirable output variable to evaluate the economic and technical efficiencies of 15 hydrogen production methods in China. The results show that the average technical efficiency and scale efficiency values of hydrogen production industry are 0.438 and 0.352, respectively, which are relevantly low. The average pure technical efficiency value is 1.090. When the strong carbon constraint is introduced, the average technical efficiency and pure technical efficiency values of hydrogen production industry are increased by about 12.3% and 22.2%, respectively, but the average scale efficiency is reduced by 1.7%. The coal-to-hydrogen production with carbon capture, utilization and storage (CCUS) technology produces the highest technical efficiency, and the technical efficiency value and pure technical efficiency value are 1.435 and 1.679, respectively. The scale efficiency value of hydrogen production from chlor-alkali is 0.951, which is the most effective scale among all measured hydrogen production processes. The methanol-to-hydrogen production also performs great. However, hydrogen production by water electrolysis does not have the advantages of economic nor technical efficiencies. The results of the input-output slack of 15 hydrogen production methods show that the excessive production inputs and carbon dioxide emission are the main reasons for the poor efficiency evaluation results of most hydrogen production methods. The CO2 emission regulation reduces the redundancy of the cost, comprehensive energy consumption and CO2 emissions of 13 hydrogen production processes by more than four times, which affected the technical efficiency and the scale efficiency of hydrogen production industry. It indicates that CO2 emission regulation has improved the economic and technical efficiency of hydrogen production processes in China.

Data-Driven Evaluation and Optimization of Agricultural Environmental Efficiency with Carbon Emission Constraints

To cope with global carbon reduction pressure, improved agricultural production efficiency, and optimize regional sustainability, we constructed a data-driven evaluation and optimization method for agricultural environmental efficiency (AEE) under carbon constraints. This study constructs a comprehensive input-output AEE evaluation index system, incorporates carbon emissions from agricultural production processes as undesired outputs, and optimizes their calculation. The Minimum Distance to Strong Efficient Frontier evaluation model considering undesired output, and the kernel density estimation, are used to quantitatively evaluate AEE from static and dynamic perspectives. Tobit regression models are further used to analyze the driving influences of AEE and propose countermeasures to optimize AEE. The feasibility of the above methodological process was tested using 2015–2020 data from the Anhui Province, China. Although there is still scope for optimizing the AEE in Anhui, the overall trend is positive and shows a development trend of “double peaks”. The levels of education, economic development, agricultural water supply capacity, and rural management are important factors contributing to AEE differences in Anhui. Data and regression analysis results contribute to the optimization of AEE and proposes optimization strategies. This study provides extensions and refinements of the AEE evaluation and optimization, and contributes to sustainable development of regions.

Research on energy policies of Jiangxi province under the dual-carbon constraints

As a region with a good ecological foundation and a strong carrying capacity of resources and environment, Jiangxi Province of China was included in the first batch of unified and standardized national ecological civilization experimental zones as early as 2016. How to realize carbon neutrality and carbon peak was the long-term key task of Jiangxi Province and the energy policy was the main effective means for Jiangxi Province to achieve high-quality economic and social development during the 14th Five-Year Plan period. Therefore, based on Bayesian Nash equilibrium theory, a carbon game analysis between enterprises and government under double carbon constraints was established. Besides, by relying on the current situation of energy consumption and carbon emission in Jiangxi Province of China, the goal path of achieving carbon neutrality and carbon peak in Jiangxi Province was tested. According to the research, with lack of natural resources and energetic development of low-carbon economy, enterprises and governments should make transpositional consideration to balance interests and realize common development amid cooperation, thus achieving the harmonious development of society. It is necessary to deepen the understanding of peak carbon dioxide emissions and carbon neutrality, analyze how Jiangxi Province solves the challenges and realization pathways under the goal of peak carbon dioxide emissions and carbon neutrality and discuss policy demands, which are of vital significance for the realization of low-carbon transformation and upgrading of China’s economy and society.

Denitrification performance, bioelectricity generation and microbial response in microbial fuel cell – constructed wetland treating carbon constraint wastewater

For the deep reduction of nitrogen, the microbial fuel cell-constructed wetland (MFC-CW) was conducted for treating carbon constraint wastewater. Results indicated that nitrogen removal decreased from 94.96% to 24.96% with influent COD/TN (chemical oxygen demand/total nitrogen) from 4 to 0. MFC-CW was seriously affected by low organic wastewater. Wetland plants contributed to denitrification, with TN removal increasing from 46.13% to 64.87%. The bioenergy output showed a linear relationship with influent COD, and the maximum power density of 1.17 mW/m2 was obtained. Correlation analysis indicated that functional genera of Paenibacillus, Trichococcus, norank_KD4-96, norank_OLB14 played a crucial role in nitrogen removal. Influent COD and wetland plants affected carbon and nitrogen metabolisms, and key genes related to denitrification were more abundant in the cathode. Findings illustrated the nitrogen metabolism in MFC-CW with carbon constraint wastewater and will extend the application of MFC-CW in secondary effluent treatment from wastewater treatment plants.

External carbon costs and internal carbon pricing

The use of internal carbon prices (ICPs) is a practice by which companies voluntarily attach a hypothetical cost to their carbon emissions to help prioritize low-carbon investment projects. We find that ICP use is driven by external carbon constraints and by firms' exposure to formal carbon pricing systems, next to various firm and society characteristics. The size of the gap between countries' actual and intended emissions alone, without a translation into stringent climate policies, does not play a role. These findings inform policymakers and investors about when and why firms account for future carbon constraints internally. A key societal risk is that corporate investments are not sufficiently directed at a future low-carbon economy. Stringent climate policies that provide predictable pathways appear to help firms mitigate the misalignment of their investments by using ICPs and thereby contribute to a less erratic and less expensive transition of the energy system.

Dynamic assessment of China’s re-electrification level considering carbon peaking and carbon neutrality goals: A study based on functional data analysis

Electrification is currently the most mature technological path to carbon neutrality. However, the traditional measurement methods of the electrification levels cannot meet the development requirements of China’s electrification under the dual-carbon strategy. Thus, this study incorporated carbon constraints into the re-electrification-level evaluation system and evaluated the re-electrification level of 30 provinces in China from 2007 to 2019 using the entropy weight method. Then, the functional data analysis method was employed to further explore the dynamic variation rule of the regional re-electrification level. The evaluation results show that the top three provinces in the mean value of the re-electrification index are Guangdong (0.628), Jiangsu (0.617), and Zhejiang (0.573). The clustering results show that there are great spatial differences in the development of electrification in China. According to the fitted function curve, China’s electrification development can be roughly divided into the following four stages: uptrend, brief decline, sharp uptrend, and slight and steady decline. At present, China’s electrification is entering a new stage. To implement re-electrification further, the government and the power grid enterprises need to find the priority and breakthroughs under carbon emissions and environmental constraints. Our research results provide reference for the improvement of regional re-electrification levels in China.

Coping Decisions of Production Enterprises under Low-Carbon Economy

It aims to study the production and emission reduction decisions of production enterprises under carbon constraints. In the case of carbon constraints in production, manufacturers have four strategic choices: production within the carbon quota, adopting emission reduction technologies, purchasing carbon emission rights, and using emission reduction technologies and purchasing carbon emission rights. Based on the income model of production enterprises under four different strategies, first, under the condition of maximizing the interests of production enterprises, the optimal profit, optimal production, optimal total carbon emission, and optimal emission reduction rate under different carbon constraints are determined, and summarize its laws. Afterward, in order to further optimize corporate profits, the impact of changes in the carbon reduction scale cost and consumers’ low-carbon preference was studied. Several important conclusions are shown as follows. First, the stricter the carbon constraint policy, the greater the optimal emission reduction rate of enterprises. Second, the adoption of emission reduction technology can effectively reduce the impact of carbon constraint on output. Third, the optimal strategy is to both reduce emissions and purchase carbon emission rights, which can realize environmental economic dividends. Fourth, the lower the cost factor of the carbon reduction scale and the higher the low-carbon preference of consumers, the easier it is for firms to achieve carbon sufficiency in their production.

Scopes of carbon emissions and their impact on green portfolios

The aim of this paper is to study the performance of carbon-based portfolios when all emissions scopes are accounted for. We formalize low-carbon mean-variance portfolio strategies by integrating a carbon penalty to a constrained mean-variance optimization framework. We resort to direct and indirect emissions, split between Scopes 1–2 and Scope 3, across geographical zones (Europe and US) and data providers (Refinitiv and Carbon4 Finance). Our results show that it is possible to cut emission intensities in half at least with virtually no loss in Sharpe ratio for reasonable levels of the carbon constraint. These results are valid across various choices of risk aversions, and irrespective of emissions data provider. For a sustainability-aware investor, these low-carbon portfolios are associated to a higher level of welfare. We find that the corresponding allocations are shifted towards assets with higher returns while keeping the portfolio's volatility unchanged. Our results add to the literature contending that sustainable investing is not costly.

Decarbonization pathways of China’s provincial energy systems under carbon constraints: A case study of Guizhou Province

The increasing energy demands complicate many provinces of China’s goals to address climate change. It has become a considerable concern for local governments and practitioners to develop a regionally appropriate pathway to reduce emissions based on their resources and social development needs. Taking Guizhou Province as an example, this study aims to determine the resource allocation and techno-economic path of each sector in the energy system to 2050 under the low-carbon policies. Taking the policy target or total carbon emissions as the boundary condition, this paper depicts four potential decarbonization scenarios through a multi-sectoral open-source global energy system model. To accommodate provincial energy systems with a higher geographical resolution, the model proposed in this paper considers the ramping constraints of base-load units and increases the optimization step size to the hourly level. In addition, a quantitative assessment of the existing barriers to the low-carbon transition of the Guizhou provincial energy system and the role of flexible decarbonization technologies are presented. Results show that the model proposed in this paper can accurately reflect the actual situation of Guizhou Province and significantly improve the sensitivity. Based on the analyses in this study, the policy scenario does not allow Guizhou province to meet the national climate target. In contrast, the other three scenarios show clearer and rapider emission reduction options. Compared to the policy scenario (PS), the investment cost increases to 26.3%, 38.3%, and 112.7% for the accelerated transition scenario (ATS), 2 °C, and 1.5 °C scenario, with a 24.7%, 35.2%, and 74.6% reduction in total emissions, respectively, indicating that a decarbonization pathway that combines ATS and 2 °C scenarios is the most appropriate way to balance economic and emission reduction benefits. This way calls for the status quo year to immediately begin deploying emission reductions, accelerating renewable energy development, and deploying appropriately affordable carbon capture and storage (CCS) and energy storage.

The carbon footprint and cost of coal-based hydrogen production with and without carbon capture and storage technology in China

CCS (CO 2 capture and storage) technology provides technical support for low-carbon hydrogen production from coal. This study evaluates the carbon footprint and cost of coal-based hydrogen production with and without CCS in China by introducing provincial coal prices and electricity prices, as well as the life cycle greenhouse gas emission factor for consumed electricity. The results show that the carbon footprint of coal to hydrogen is reduced by 52.34%–74.59% to 4.92–10.90 CO 2 eq/kg H 2 after installing CCS technology, which is close to that of solar electricity-based hydrogen production. In addition, CCS increases the cost by 44.59%–60.84% to 1.44–2.11 USD/kg H 2 , but it does not deprive the cost advantage of hydrogen production from coal over renewable electricity-based hydrogen production. Therefore, China should promote the development of coal to hydrogen with CCS to meet the growing demand for hydrogen, at least before there is a breakthrough in hydrogen production from renewable electricity. West China may be the preferred option for future new coal to hydrogen plants, where the cost of coal to hydrogen with CCS is lower than that of other regions. Of course, other complex factors, such as the regional distribution of demand, must be considered to truly determine the site. In addition, coal-to-hydrogen plants in Inner Mongolia, Xinjiang and Shaanxi Provinces, with low hydrogen production costs , strong carbon constraints and low CCS costs, should be the priority option for early CCS projects in China. • The carbon footprint of coal to hydrogen without CCS is 19.37–22.87 CO 2 eq/kg H 2 . • CCS technology can reduce the carbon footprint by 52.34%–74.59%. • The cost of coal to hydrogen without CCS is 0.90–1.46 USD/kg H 2 . • The installation of CCS will increase the cost by 44.59%–60.84%.

Microbial Carbon Use Efficiency in Coastal Soils Along a Salinity Gradient Revealed by Ecoenzymatic Stoichiometry

AbstractSoil salinity affects the microbial carbon use efficiency (CUE) that in turn regulates soil‐atmosphere gas exchange and soil C sequestration. So far, little is known about CUE in salt‐affected soils. Hereby, CUE across coastal soils with electric conductivity (EC) ranging from 0.14 dS m−1 to 13.65 dS m−1 was investigated using stoichiometric modeling. Contrary to the belief that CUE decreases with salinity increase, this present study showed that CUE follows a unimodal pattern along the saline gradient with the highest CUE observed at EC = 2 dS m−1. When EC > 2 dS m−1, both CUE and soil microbial growth rate significantly decrease with salinity indicating that microorganisms sacrifice growth for survival/adaptation to high salt stress. When EC < 2 dS m−1, CUE shows a significant positive correlation with EC, but microbial community growth rates remain stable. We also observed that microbial growth becomes limited by carbon scarcity and that the degree of carbon constraint is significantly aggravated with EC. This implies that elevating CUE can be the preferred mechanism for microorganisms to stabilize elemental stoichiometry and maintain growth rate. Our results revealed that along the saline gradient, the dominant factor of CUE shifted from the ecological stoichiometry to salinity, which provides an insight into the microbial‐driven carbon cycling in salt‐affected soils.

Research on Extension Design of Emergency Cold Chain Logistics from the Perspective of Carbon Constraints

Extenics has unique advantages in solving contradictions by using formal models to explore the possibility of expanding things and the laws and methods of development and innovation. This paper studies the specific application of the extension strategy generation method in emergency cold chain logistics, in order to solve the problem that the emergency plan is difficult to cover in the face of an emergency. The purpose of this paper is to provide ideas for the generation of strategies to solve the contradictions of cold chain logistics in complex emergency scenarios. Giving full play to the unique advantages of extenics in solving contradictory problems, this paper analyzes the core problems, objectives and conditions of emergency cold chain logistics in four links with the case scenario of the COVID-19 pandemic outbreak, extends and generates 10 measures to form 36 schemes, and evaluates the combination schemes quantitatively and objectively using the dependent function and superiority evaluation formula. In addition, the consideration of carbon constraints is added to the selection of the scheme, and the specific plan of integrating e-commerce platform, expert guidance, establishing temporary cold storage transfer and contactless distribution is designed. The research results provide support for meeting the needs of emergency logistics schemes in different situations and optimizing the energy efficiency of the scheme while ensuring humanitarian support. At the same time, the application of extenics basic-element formal language also provides a reference for further applying artificial intelligence to the design of emergency logistics schemes.

Using emissions trading schemes to reduce heterogeneous distortionary taxes: The case of recycling carbon auction revenues to support renewable energy

We examine the economic impacts of using the revenues from environmental taxation to reduce a pre-existing distortionary tax in a multisector economy where the environmental regulation and pre-existing distortionary tax apply heterogeneously across polluting sectors. With a numerical framework including a detailed sectoral disaggregation, we quantify these in the specific case of the European Union where carbon pricing coexists with electricity levies employed to support renewable energy. We find that using auction revenues from the EU Emissions Trading System (ETS) to reduce the national levies results in a 1.8% ETS carbon price increase but a 5.9% drop in the non-ETS carbon constraint. While the energy intensive sectors often benefit from electricity levy exemptions, the combination of these exemptions and of the recycling of carbon auction revenues to support renewable energy makes the ETS sectors worse off than if carbon revenues are transferred to households. In aggregate, the recycling option analysed here results in a GDP gain due to its impacts on the non-ETS sectors, the reduction of the electricity levy and associated distortionary effects.

Spatial-temporal characteristics of cultivated land use eco-efficiency under carbon constraints and its relationship with landscape pattern dynamics

With China’s rapid urbanization and socioeconomic development, cultivated land has been experiencing continuous reduction, spatial shift, and ecological degradation in the past few decades, coupled with the decreasing and aging agricultural labor force, thus improving cultivated land utilization efficiency while minimizing associated environmental impacts is one of the major challenges facing China. This study first establishes an index system to access the eco-efficiency of cultivated land use (EECLU) considering both carbon sequestration and carbon emissions, next, employs the Super-SBM model to evaluate the spatial–temporal characteristics of EECLU, then, uses landscape metrics to describe landscape pattern dynamics of cultivated land, and finally conducts panel data analysis to investigate the relationship between landscape pattern changes and the EECLU variations. This framework was applied to Jiangsu Province, a major grain-producing area with a developed economy. The results showed that the average EECLU of the counties in rural Jiangsu from 2000 to 2020 slightly decreased, most counties were in the medium–low efficiency range (0.6–0.8), and Southern Jiangsu generally has the lowest EECLU values while Central Jiangsu has the highest. The total area and the average patch sizes of cultivated land have significantly positive impacts on EECLU, while the shape complexity and patch fragmentation have significantly negative impacts on EECLU. Therefore, comprehensive low-carbon strategies and better management and technology in the agricultural sector are needed to further improve EECLU. On the premise of protecting the total area of cultivated land, larger and more aggregated land patches should be promoted in the land consolidation process, and the cultivated land targeted for occupation or compensation should be able to simplify the overall shape of cultivated land patches.