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Scientific analysis of regional agricultural carbon emission prediction models and empirical studies are of great practical significance to the realization of low-carbon agriculture, which can help revitalize and build up ecological and beautiful countryside in China. This paper takes agriculture in Guangdong Province, China, as the research object, and uses the extended STIPAT model to construct an indicator system for the factors influencing agricultural carbon emissions in Guangdong. Based on this system, a combined Isomap–ACO–ET prediction model combing the isometric mapping algorithm (Isomap), ant colony algorithm (ACO) and extreme random tree algorithm (ET) was used to predict agriculture carbon emissions in Guangdong Province under five scenarios. Effective predictions can be made for agricultural carbon emissions in Guangdong Province, which are expected to fluctuate between 11,142,200 tons and 11,386,000 tons in 2030. And compared with other machine learning and neural network models, the Isomap–ACO–ET model has a better prediction performance with an MSE of 0.00018 and an accuracy of 98.7%. To develop low-carbon agriculture in Guangdong Province, we should improve farming methods, reduce the intensity of agrochemical application, strengthen the development and promotion of agricultural energy-saving and emission reduction technologies and low-carbon energy sources, reduce the intensity of carbon emissions from agricultural energy consumption, optimize the agricultural planting structure, and develop green agricultural products and agro-ecological tourism according to local conditions. This will promote the development of agriculture in Guangdong Province in a green and sustainable direction. [ABSTRACT FROM AUTHOR]

Carbon emission trading scheme (ETS) policy is a quota-based market-motivated environmental regulation policy that by transforming environmental responsibility of enterprises into self-consciousness behavior induces green innovation behavior in enterprises and influences the level of regional green innovation. Based on panel data on green patents in 30 Chinese provinces from 2010 to 2019, the paper uses six Chinese pilot provinces for ETS as the treatment group to investigate the induced effects of Chinese ETS policy on the quantity and quality of green technology innovation using difference in difference model and analyzes the effects of Chinese ETS policy on green technology innovation in different pilot regions using a synthetic control method. The findings indicate that (1) compared with non-pilot areas, the carbon emission trading policies in pilot areas can induce green technology innovation activities. (2) In all pilot regions, ETS policies significantly promoted the quantity and quality of green technology innovation, but the quantity promotion effect was significantly better than the quality promotion effect, and the low-quality promotion effect was better than the high-quality promotion effect. (3) In terms of individual pilot region, ETS policies in Guangdong, Hubei and Tianjin significantly promoted the quantity of green technology innovation and low-quality green technology innovation, but significantly inhibited or suppressed high-quality green technology innovation in a short term. This paper not only enriches the research on environmental equity trading policies and green technology innovation theoretically, but also provides empirical evidence for the effectiveness of carbon emission trading schemes pilots program in China. [ABSTRACT FROM AUTHOR]

It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model's validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices. [ABSTRACT FROM AUTHOR]

Carbon sequestration in farmland ecosystems is an important link in the world carbon cycle and plays an important role in regional carbon reduction. Guangdong, a major industrial and economic province in China, was used as the study area, and the period 2001–2020 was taken as the study period. The carbon emissions, sequestration, and footprint of farmland ecosystems in Guangdong were estimated using carbon emission factors for agricultural inputs that are closer to the actual situation in China. The study showed that: (1) Carbon average emissions from farmland in Guangdong during the study period was 3.7624 million t a−1, with a balanced overall trend of change, and that nitrogen fertilize applications was the main factor contributing to carbon emissions. (2) The carbon sink capacity of Guangdong farmland ecosystems showed an overall decreasing trend of 10.32%, with an average annual carbon sink of 19.0363 million t a−1. Paddy and sugar cane cultivations were the main factor of carbon sink in farmland. (3) The average annual carbon footprint of Guangdong's farmland ecosystems was 531,100 ha a−1, which was in a carbon surplus. Carbon surplus and footprint showed a decreasing trend year by year. The paper results provide a theoretical basis for the formulation of carbon emission reduction policies and industrial restructuring in Guangdong and provinces with the same industrial structure. [ABSTRACT FROM AUTHOR]

The variation in the urban spatial structure (USS) has profound impacts on carbon emissions. Studying the relationship between the two can provide guidance for carbon neutrality strategies and the construction of low-carbon cities in China. However, there is currently a lack of comparative research on the different regions within a province. In this paper, the spatiotemporal evolution of the USS and carbon emissions, at five-year intervals from 2000 to 2020, is investigated in 21 prefecture-level cities in Guangdong Province, China, and the overall relationship of the USS to carbon emissions and their spatiotemporal variations are analyzed by using a two-way fixed-effects model and a geographically and temporally weighted regression model, respectively. The results show that, first, over the past twenty years, the scale of cities has continued to expand, with increasing continuity and aggregation in the built-up areas, while the complexity and fragmentation of their shapes have gradually decreased. Second, the gap in carbon emissions between the Pearl River Delta and other regions in Guangdong shows a trend of first decreasing and then increasing, with high values concentrated in the Pearl River Delta region and the city of Shantou in the east. Third, compared to socio-economic factors, the USS has a more direct and pronounced impact on carbon emissions. Urban expansion and the increased complexity of land patches promote carbon emissions, whereas improving urban spatial continuity and compactness can reduce carbon emissions. Fourth, the dominant spatial structure indicators of carbon emissions differ among the regions of eastern, western, and northern Guangdong and the Pearl River Delta. This study proposes spatial optimization strategies for the low-carbon development of cities in Guangdong Province, providing a new perspective for integrating urban layout and emission reduction policies. [ABSTRACT FROM AUTHOR]

Against the backdrop of severe global warming, the low-carbon city pilot policy, with carbon emission reduction as its main objective, is an important initiative for China to fulfil its international commitment to carbon emission reduction and promote a green and low-carbon development strategy. However, none of the literature has yet evaluated whether the pilot low-carbon city policy promotes urban land use efficiency as a policy effect. In view of this, this paper measures urban land use efficiency from a low-carbon perspective using a global reference super-efficiency SBM model based on data from 186 prefecture-level cities in China from 2005–2017, and subsequently constructs a difference-in-differences method to test the true impact of low-carbon city pilot policies on urban land use efficiency and carbon emissions, and uses a propensity score matching method to test its robustness. It is found that: (1) the average level of urban land use efficiency in China is low and on a downward trend; (2) overall, cities are predominantly low-efficiency cities, with only the high-efficiency cities in Guangdong Province showing spatial agglomeration; and (3) the low-carbon city pilot policy reduces carbon emissions while also negatively affecting urban land use efficiency. Accordingly, this paper puts forward corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

As a flexible market mechanism based on the control of total quantity, the carbon emission trading system aims to achieve economic development while reducing carbon emissions and energy consumption. How to improve the effectiveness of carbon emission trading (CET) policy and achieve the established emission reduction targets have attracted widespread attention from scholars. Taking Guangdong Province as an example, this paper constructed a system dynamics (SD) model to investigate the interaction between internal factors of CET system and simulated the effectiveness of CET policy from 2016-2026. The results indicate that (1) Relative errors between historical data and simulated data are controlled within 5%, which indicates that the system model is suitable to simulate real system. (2) The average sensitivity of the quota variation rate, paid ratio and penalty coefficient are 0.42, 0.56 and 0.29, respectively, indicating that these three parameters can be identified as leverage parameters affecting the efficiency of the CET policy. (3) A single type of policy is difficult to achieve emission reduction targets. The policy portfolio scenario will achieve the reduction target in Thirteenth Five-Year plan of Guangdong, that is, the total amount of quota decreases by 0.5% per year, the paid ratio rises 2% in that ratio per year, and the penalty coefficient is triple the carbon price. [ABSTRACT FROM AUTHOR]

In response to global warming, China has formulated the "double carbon" strategic goals of peaking carbon dioxide emissions before 2030 and reaching carbon neutrality before 2060. The problem of rural carbon emissions is often ignored due to underdeveloped industries and services. In this paper, the carbon emission pathways in the rural areas of Guangdong Province are investigated. Since energy consumption is the main source of carbon emissions, the factor analysis was used to analyze the main factors affecting rural household energy consumption and agricultural production energy consumption. Multiple linear regression was conducted to predict the rural energy consumption demand in Guangdong. Furthermore, the current situation and development trend of rural energy supply, demand and consumption structure, and the potential of renewable energy development were considered to predict carbon emissions in the rural areas of Guangdong. Moreover, the carbon emission pathways in the rural areas of Guangdong were discussed under two scenarios: the base scenario and the radical model. [ABSTRACT FROM AUTHOR]

In recent years, due to the rise in energy prices and the impact of COVID-19, energy shortages have led to unsafe power supply environments. High emissions industries which account for more than 58% of the carbon emissions of Guangdong Province have played an important role in achieving the carbon peak goal, alleviating social energy shortage and promoting economic growth. Controlling high emissions industries will help to adjust the industrial structure and increase renewable energy investment. Therefore, it is necessary to comprehensively evaluate the policies of energy security and the investments of high emission industries. This paper builds the ICEEH-GD (comprehensive assessment model of climate, economy, environment and health of Guangdong Province) model, designs the Energy Security scenario (ES), the Restrict High Carbon Emission Sector scenario (RHS) and the Comprehensive Policy scenario (CP), and studies the impact of limiting high emissions industries and renewable energy policies on the transformation of investment structure, macro-economy and society. The results show that under the Energy Security scenario (ES), carbon emissions will peak in 2029, with a peak of 681 million tons. Under the condition of ensuring energy security, the installed capacity of coal-fired power generation will remain unchanged from 2025 to 2035. Under the Restrict High Carbon Emission Sector scenario (RHS), the GDP will increase by 8 billion yuan compared with the ES scenario by 2035. At the same time, it can promote the whole society to increase 10,500 employment opportunities, and more investment will flow to the low emissions industries. In the Comprehensive Policy scenario (CP), although the GDP loss will reach 33 billion yuan by 2035 compared with the Energy Security scenario (ES), the transportation and service industries will participate in carbon trading by optimizing the distribution of carbon restrictions in the whole society, which will reduce the carbon cost of the whole society by more than 48%, and promote the employment growth of 104,000 people through industrial structure optimization. Therefore, the power sector should increase investment in renewable energy to ensure energy security, limit the new production capacity of high emissions industries such as cement, steel and ceramics, and increase the green transition and efficiency improvement of existing high emissions industries. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrogen is a type of clean secondary energy, and the hydrogen energy supply is vital to regional energy development. Guangdong is at the forefront of hydrogen energy deployment in China, and aims to achieve peak carbon by 2030. However, hydrogen production research is challenging due to complex technical routes, technical maturity levels, life-cycle carbon emissions, and costs. Multi-time-scale research is necessary to explore optimal production paths and their contributions to CO 2 emission reduction. This study discussed key technologies and the technical maturity of five hydrogen production routes: production from industrial byproducts and coal, wind, hydro-, and photovoltaic power. Policy scenarios were designed to investigate the potential of these routes and technology deployment intensity under medium- and long-term scenarios. This paper proposes a roadmap for hydrogen development and provides a reference for hydrogen production planning in Guangdong. This methodology can be applied in other regions for hydrogen energy development research. • Key technology maturity of the hydrogen production routes was evaluated. • Roadmap of hydrogen production in Guangdong Province was designed. • A method for research of hydrogen supply scenarios was proposed. [ABSTRACT FROM AUTHOR]

This paper explores the forecasting power of some of the most informative indicators of economic uncertainty on carbon emission price volatility. We use one- and two-component GARCH-MIDAS models based on mixed frequency data and Model Confidence Set (MCS) testing with 'rolling scheme' forecast method to examine the forecasting performance of economic uncertainty indicators. We employ an economic policy uncertainty (EPU) indicator with China, US and global economic policy uncertainty constituents together with traditional uncertainty indicators, such as macroeconomic and financial market volatility. Our empirical findings show that generally economic policy uncertainty indicators contain more information of carbon emission price volatility than other indicators. Specifically, one-component GARCH-MIDAS model with the EPU indicator with China constituent and two-component model with EPU indicator have superior performance in forecasting carbon emission price volatility of the Guangdong pilot in China. Our study adds insights into factors that affect carbon emission price movements. [ABSTRACT FROM AUTHOR]

The urgency of addressing climate change and reducing carbon emissions necessitates the establishment of effective carbon markets. Our paper focuses on assessing and interpreting the efficiency of carbon markets, which is a crucial aspect of their successful implementation. We selected Hubei, Shenzhen, and Guangdong carbon pilots as examples to measure their dynamic information efficiency by combining the rolling time window and wild-bootstrap variance ratio test. Furthermore, we combined SHapley Additive exPlanations (SHAP) and Extra Trees (ET) regressor to reveal the nonlinear relationship between market performance and efficiency. Our findings highlight the time-varying nature of carbon market efficiency and reveal that China's carbon market has not yet achieved stable weak-form efficiency. Additionally, we identified excessive trading volume volatility as a significant constraint on market efficiency, while the trading volume itself exhibits weak explanatory power. Our proposed hybrid model demonstrates superior predictive and explanatory capabilities, offering regulators a valuable tool for continuously monitoring and interpreting carbon market efficiency. Finally, we suggested that reducing trading volume volatility should be a major concern for regulators to optimize market operations at present. [ABSTRACT FROM AUTHOR]

Copyright of Advances in New & Renewable Energy is the property of Editorial Office of Advances in New & Renewable Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global warming caused by carbon emissions has become increasingly prominent. As the world's second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p , d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures. [ABSTRACT FROM AUTHOR]

This paper constructs an embodied carbon model in inter-provincial trade based on China's inter-provincial multi-regional input-output table. By analyzing the embodied carbon emissions for Guangdong with the forefront of economic development, this research reveals the impact of trade scale and structure between Guangdong Province and other provinces on embodied carbon emissions, and finds out key provinces and sectors for emission reduction. It can enable national and regional economic and environmental policies to be implemented into specific industries, guide capital flow to key sectors for emission reduction, provide comments for relevant national and provincial decision-making sectors to adjust trade structure, and play the role of inter-provincial trade in energy conservation and emission reduction. [ABSTRACT FROM AUTHOR]

Vehicle exhaust is one of the main sources of carbon emissions. The short-term traffic flow prediction plays an important role in alleviating traffic congestion, optimizing the travel structure, and reducing traffic carbon emissions. The current advanced models of short-term traffic flow prediction are evaluated in this work, especially their inadequacies. To improve the prediction accuracy and ensure fine traffic management, an effective self-attention-based hybrid model is proposed to predict the short-term traffic flow. The proposed model includes an encoder-decoder neural network module and a self-attention mechanism module. The self-attention mechanism module is applied as a feature extraction unit in this hybrid model to enhance the ability of key information capture and to settle the problem on key information disappearing due to the increasing sequence length in traditional models. The dataset of the Guangdong freeway toll station is used for the experimental testing. Compared with several baseline models, the proposed model is more suitable for real-time prediction and can provide highly accurate results. Also, a better interpretability is presented in this proposed model. The experimental results showed that MAE, RMSE, and MAPE of the proposed model are 3.01, 4.38, and 12.99%, respectively. Our new hybrid model gives a higher accuracy than the support vector regression (SVR) model, LSTM neural network-attention (LSTM-attention) model, and temporal convolutional network (TCN) model. It shows that the proposed model in this work is favorable to the short-term traffic flow prediction. [ABSTRACT FROM AUTHOR]

Environmental degradation and climate change are two important issues that threaten human survival and sustainable social development. Based on the characteristics of the same source of sulfur dioxide and greenhouse gases, we analyze the collaborative governance mechanism of carbon emission trading on sulfur dioxide. The results show that carbon emission reduction trading may not necessarily deliver sulfur dioxide reduction co-benefits. Then, we conduct an empirical using data of 30 regions in China from 1998 to 2017. The results show that the co-benefits of carbon emission trading in terms of the reduction of sulfur dioxide are regionally heterogeneous. The carbon emission trading policies implemented in Beijing, Chongqing, Hubei, and Guangdong have realized collaborative governance on sulfur dioxide. However, the carbon emission trading policies implemented in Tianjin and Shanghai have not realized collaborative governance on sulfur dioxide. Moreover, Chongqing has the highest average annual sulfur dioxide reduction co-benefits, followed by Guangdong. The gap of sulfur dioxide reduction co-benefits between Beijing and Hubei's synergistic emission reduction effect is small. Moreover, the sulfur dioxide reduction co-benefits of carbon emission trading are mainly achieved by reducing fossil energy consumption. Meanwhile, the carbon emission trading implemented in Beijing also delivers sulfur dioxide reduction co-benefits by improving pollution reduction technology. [ABSTRACT FROM AUTHOR]

As the primary source sector of carbon emissions, the power industry's green innovation is an indispensable component of China's low-carbon transformation. This paper took China's Carbon Emission Trading Scheme as a quasi-natural experience and adopted the synthetic control method to evaluate the impact of Carbon Emission Trading Scheme pilot policy on power industry's green innovation based on the provincial industrial level data. Furthermore, this paper examined the impact mechanisms of Carbon Emission Trading Scheme on the power industry's green innovation through policy comparison, moderating and mediating effect analyses. The research findings show that: 1) there is heterogeneity in Carbon Emission Trading Scheme's effectiveness in inducing green innovation in China, the power industry only in Beijing and Guangdong has been significantly promoted, while other pilots have not; 2) the success of Beijing and Guangdong can be attributed to distinctive features of their carbon market and power industry, including the refined quota accounting mechanism, the application of paid quota allocation, the larger enterprise scale and more research and development investment; 3) enterprise scale and research and development investment have positive moderating effect and mediating effect in the promotional effect of the Carbon Emission Trading Scheme on green innovation respectively. Finally, some policy recommendations are put forward to promote the power industry's green innovation and carbon market construction. • Evaluate the impact of China's CETS on the power industry's green innovation in each pilot. • The CETS effects among pilots are heterogeneous, only significant in Beijing and Guangdong. • Rational CETS mechanism design could improve the power industry's green innovation. • Enterprise scale and R&D investment positively moderate and mediate the CETS effect. [ABSTRACT FROM AUTHOR]

This paper investigates the long-term dynamic cross-correlation evolution between US economic policy uncertainty index (USEPU) and Guangdong carbon emission trading price (GDCP) from the multifractal detrended cross-correlation analysis (MF-DCCA) perspective. With the calculation of correlation statistics and fluctuation function, the beginning procedures of MF-DCCA, we find that the cross-correlation between USEPU and GDCP is significant and presents power law property. Also, with the Hurst exponent, we find that the long-horizon correlations between series are persistent. Moreover, we perform Rényi exponent and spectrum singularity check. The empirical findings reveal that the all the correlations are of multifractality and the correlation of GDCP holds the highest degree. [ABSTRACT FROM AUTHOR]

• A method based on Neural Network is proposed for provincial-level CO 2 emissions estimation. • An autoencoder is proposed to downscale the provincial-level CO 2 emissions to city-level. • A complete city-level CO 2 emissions inventory of GBA and surrounding cities is complied. • Spatial dependence and agglomeration characteristics of CO 2 emissions are analyzed. • Spatio-temporal heterogeneity of factors affecting CO 2 emissions is investigated. Significant attention has been given to the issue of CO 2 emissions worldwide, especially for China as the largest emitter. Cities, as the main carriers of China's economic development, account for up to 85 % of CO 2 emissions from energy use in the country. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and surrounding cities play important leading roles in low-carbon development. However, compiling city-level CO 2 emissions is challenging due to the limited availability of data. In this paper, a new idea is proposed by using SSA-BP for estimating provincial-level CO 2 emissions and downscaling provincial-level CO 2 emissions to city-level CO 2 emissions by reconstructing missing values of city-level CO 2 emissions with an autoencoder. This study compiles a city-level CO 2 emissions inventory and conducts a spatio-temporal analysis of CO 2 emissions in the GBA and surrounding cities. The results show that CO 2 emissions exhibit a spatial distribution pattern with spatial dependence. Furthermore, according to the analysis of spatio-temporal heterogeneity, GDP influenced CO 2 emissions the most within the five influencing factors, and the influences of GDP, population, and energy intensity were mainly positive, while the influence of patents was mainly negative, and that of trade was both positive and negative. [ABSTRACT FROM AUTHOR]

As a major carbon emitter, the power sector plays a crucial role in realizing the goal of carbon peaking and carbon neutrality. This study constructed a low-carbon power system based on the LEAP model (LEAP-GBA) with 2020 as a statistic base aiming of exploring the low-carbon transformation pathway of the power sector in the Guangdong–Hong Kong, and Macao Greater Bay Area (GBA). Five scenarios are set up to simulate the demand, power generation structure, carbon emissions, and power generation costs in the power sector under different scenarios. The results indicate that total electricity demand will peak after 2050, with 80% of it coming from industry, buildings and residential use. To achieve net-zero emissions from the power sector in the GBA, a future power generation mix dominated by nuclear and renewable energy generation and supplemented by fossil energy generation equipped with CCUS technologies. BECCS technology and nuclear power are the key to realize zero carbon emissions from the power sector in the GBA, so it should be the first to promote BECCS technology testing and commercial application, improve the deployment of nuclear power sites, and push forward the construction of nuclear power and technology improvement in the next 40 years. [ABSTRACT FROM AUTHOR]

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. The carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China's future carbon quota allocation will gradually transition from free allocation to auction allocation, and the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism, constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model, systematically researches the power generation structure under different auction ratios with the time scale of months, and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions. The results of the study show that after comprehensively comparing the total economic cost, renewable energy development, and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits. [ABSTRACT FROM AUTHOR]

In the context of China's dual‑carbon goals and electricity market reforms, the high penetration of renewable energy sources (RES) in a single microgrid (MG) imposes challenges to its flexible operation. This paper addresses the cooperation within a virtual power plant (VPP) aggregated by multiple MGs. The VPP, managed by the VPP operator, acts as an intermediate entity to facilitate MGs' economic and low-carbon operation. To ensure the interests of various stakeholders in the VPP, this work proposes a low carbon oriented tri-stage collaborative energy management framework. In the 1st stage, the day-ahead optimal scheduling of individual MGs and the bidding strategy of the VPP operator are modeled, where an internal pricing method is incorporated. The 2nd stage addresses the real-time redispatch of the VPP operator using a receding horizon-based model predictive control (MPC) solution. This aims to satisfy the real-time operational requirements of MGs and reduce adjustment costs. Furthermore, in the 3rd stage, a penalty allocation mechanism is established for MGs based on their forecasting errors, to protect the VPP operator's interests. The effectiveness of the proposed solution is evaluated through comparative simulations in the context of the Guangdong electricity market. • The virtual power plant (VPP) aggregated by multiple microgrids (MG) is studied. • The quantification of carbon emission is involved in the whole scheduling process. • An peer-to-peer (P2P) electricity trading pricing method is improved and adopted. • A penalty allocation mechanism is proposed to guarantee interests of each entity. • The Guangdong electricity market is considered as the market mechanism. [ABSTRACT FROM AUTHOR]

Promoting urban green growth necessitates a dual focus on reducing carbon emissions and fostering high-quality development. However, the body of research on the coupling relationship between high-quality urban development and carbon emissions remains remarkably limited. Taking Guangdong province as an example, this study strived to establish a comprehensive evaluation system for high-quality urban development encompassing economic, societal, and ecological dimensions and further conducted an in-depth examination of the spatiotemporal pattern and driving forces of coupling coordination degree between high-quality urban development and carbon emission intensity during 2000–2017. The coupling coordination degree in Guangdong province has shown continuous growth, transitioning from moderate incoordination to moderate coordination. The coupling coordination degree showed the overall spatial distribution characteristics of "high in southeast and low in others", with Zhuhai, Zhongshan, Foshan, Guangzhou, Dongguan, and Shenzhen as the core. Notably, technological advancement, environmental governance, and economic development emerge as pivotal factors that positively affect carbon emission intensity reduction, environmental quality improvement, and coupling coordination enhancement. This research provides valuable insights for achieving harmonized high-quality development in Guangdong province involving policies of regional differences, industrial competitiveness, and new-type urbanization. [ABSTRACT FROM AUTHOR]

To propel the green transformation of the transportation industry in Guangdong Province. Against the backdrop of the five-year plan, this study employs the Tapio decoupling model and the Logarithmic Mean Divisia Index decomposition method to analyze the decoupling status and driving factors of carbon emissions from transportation and economic growth in Guangdong Province from 2001 to 2020. The results show that:(1) Both the total volume of carbon emissions from transportation and per capita emissions exhibit an overall upward trend, with petroleum-based emissions accounting for an overwhelming 96%, with diesel emissions register the highest proportion and most substantial increase. (2) The decoupling status predominantly manifests as weak decoupling, with intermittent years expansive coupling,, expansive negative decoupling, strong decoupling, and recessive coupling, thus indicating a persistent state of decoupling instability. (3) The study introduces the urbanization effect, wherein the effects of income urbanization, as well as spatial urbanization, predominantly exert positive driving influences. Conversely, the effects of energy intensity, population urbanization, industry scale, and energy structure collectively exert negative driving influences. Notably, the effect of income urbanization emerges as the primary factor propelling the augmentation of carbon emissions, while the energy intensity effect serves as the primary factor curbing such escalation. Finally, pertinent policy recommendations are put forth. [ABSTRACT FROM AUTHOR]

Emission Trading Scheme (ETS) is one of the most important instruments introduced to meet carbon neutral targets internationally. To reduce carbon emissions, China has established eight regional ETS since 2013 to reduce carbon emissions. However, significant heterogeneities exist across the eight pilots, resulting in distinctively different carbon price movements. Traded as a financial asset, carbon allowance is vulnerable to internal and external shocks. Though carbon prices have been fluctuating fiercely since the inception of the carbon market, little is known about potential carbon price bubbles in any of these pilots. To this end, this paper resorts to the generalized sup Augmented Dickey-Fuller (GSADF) test, which allows the detection of multiple bubbles, as well as the date stamping of bubbles in carbon prices. For the first time, price bubbles are found in the Chinese carbon markets. The empirical results find three bubbles in Guangdong pilot, two bubbles in Tianjin pilot, and one bubble in Hubei pilot. These explosive episodes are closely related to immature market mechanisms and policy implementations. The results provide insightful implications for the upcoming unified national carbon market. • This paper detects the carbon price bubbles in Chinese markets. • Multiple bubbles exist in different pilots. • Not all pilots encounter bubbles. [ABSTRACT FROM AUTHOR]

• A source–sink matching LCA–MILP method is proposed. • The unit cost of net CO 2 reduction ranges from 237.81 CNY/t to 253.51 CNY/t. • In Guangdong Province, China, the distribution of the CCUS supply chain shows a pattern of spreading from coastal to inland regions as the scale increases. • The implementation of carbon trading policies effectively reduces the costs of CO 2 emissions reduction initiatives. • Monte Carlo simulation is performed to evaluate the uncertainty in the supply chain. Carbon capture utilization and storage technology has been identified as a key solution for limiting the global temperature increase to 2 °C from preindustrial levels. In this study, we develop a source–sink matching mixed-integer linear programming model to obtain an optimal cost for the Carbon capture utilization and storage supply chain and evaluate the environmental benefits using life cycle assessment. Guangdong Province, China, with annual CO 2 emissions of 585 Mt, is chosen as the case study for this paper. The results show that the deployment of the carbon capture utilization and storage supply chain follows a pattern of spreading from coastal to inland regions as the CO 2 net reduction rate increases. Moreover, the unit cost of CO 2 reduction increases with the CO 2 reduction rate, specifically, from 237.81 Yuan per ton to 253.51 Yuan per ton as the CO 2 reduction rate increases from 10% to 60%. The CO 2 -derived methanol is not economically viable, while having low implied carbon emissions, it has approximately 2.5 times the unit cost of traditional methanol. We recommend promoting carbon trading due to its effectiveness in reducing the unit abatement cost. Additionally, the carbon price level of 220 Yuan/t can achieve a profitable supply chain at various reduction rates. However, carbon labeling subsidies have little effect on reducing methanol production costs. The 95% confidence intervals for unit abatement costs obtained from Monte Carlo simulations can provide robust support for policymakers. [ABSTRACT FROM AUTHOR]

• A novel carbon emission reduction(CER) methodology is proposed. • The Black-Scholes mode is introduced to measure carbon emission option pricing. • Case studies of campus grid CER assessment are conducted in Shenzhen. • Methods and policy suggestions of CER are given for grid integration of EVs. China, as the world's largest emitter of carbon emissions, has implemented carbon markets to reduce carbon emissions, but the economic feasibility of integrating electric vehicles (EVs) into these markets remains uncertain. To address this challenge, we propose a novel methodology for grid integration of EVs based on the Shenzhen carbon inclusive management measures. Our proposed methodology can optimize EV charging schedules based on carbon emission prices and improve the economic feasibility of low-carbon transitions through EVs in China. We incorporate the Black-Scholes model to measure carbon emission option pricing and develop an optimal bidding strategy that considers spatiotemporal uncertainties of EVs and electricity markets. We use numerical simulations with real-world data from Shenzhen carbon emission market and Guangdong electricity market to demonstrate the effectiveness of our proposed methodology. Results reveal that scheduling 100 EVs' charging/discharging behavior properly can generate a revenue of 2379.4 yuan for the distribution system operator, while the revenue for carbon emission reduction (CER) is only 9.35 yuan. This paper demonstrates that the potential revenue of CER may be substantially based on the future scenario for the carbon emission market. These findings provide valuable insights for policymakers and stakeholders seeking to reduce carbon emissions in China's electricity sector. [ABSTRACT FROM AUTHOR]

Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in the formulation of competition strategies. This investigation has established a hybrid CTP forecasting framework combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) method, improved salp swarm algorithm (ISSA), and multi-kernel extreme learning machine (MKELM) methods to improve forecasting accuracy. Firstly, the initial CTP data sequence is disintegrated into several intrinsic mode functions (IMFs) and a residual sequence by a CEEMDAN method. Secondly, to save calculation time, SE method has been utilized to reconstruct the IMFs and the residual sequence into new IMFs. Thirdly, the new IMFs are fed into the MKELM model, combing RBF and the poly kernel functions to utilize their superior learning and generalization abilities. The parameters of the MKELM model are optimized by ISSA, combining dynamic inertia weight and chaotic local searching method into the SSA to enhance the searching speed, convergence precision, as well as the global searching ability. CTP data in Guangdong, Shanghai, and Hubei are selected to prove the validity of the established CEEMDAN-SE-ISSA-MKELM model. Through a comparison analysis, the established CEEMDAN-SE-ISSA-MKELM model performs the best with the smallest MAPE and RMSE values and the highest R2 value, which are 0.76%, 0.53, and 0.99, respectively, for Guangdong,. Thus, the presented model would be extensively applied in CTP forecasting in the future. [ABSTRACT FROM AUTHOR]

The digital economy plays an important role in the high-quality development of cities, and low-carbon urban development is an integral part of this evolution. Therefore, it is important to explore the impact of the development of the digital economy on urban carbon emissions. By considering 21 cities in Guangdong Province as the object of the research, this study measured the levels of the digital economy from 2011 to 2019 by using an entropy weight-based TOPSIS model, analyzed the spatiotemporal changes, and used geographically and temporally weighted regression to examine the spatiotemporal heterogeneity of the impact of the digital economy on urban carbon emissions. The results showed the following: (1) The development of the digital economy in Guangdong Province in general exhibited a stable trend of growth, and the average level of its development in the 21 cities considered increased by 3.4 times during the study period. (2) The level of development of the digital economy in the Pearl River Delta was significantly higher than that in northern, western, and eastern Guangdong, with Shenzhen being the most developed city in this regard (0.8473), and Shanwei being the least developed (0.0633). (3) The impact of the development of the digital economy on carbon emissions had significant spatiotemporal heterogeneity. The benefits of the development of the digital economy in terms of a reduction in carbon emissions was the most significant in the Pearl River Delta, and regional differences were prominent. (4) The reductions in carbon emissions in cities with a highly developed digital economy were more significant than has been reported in past studies in the area. However, it had a negative effect on the urban carbon emissions in cities with a poorly developed digital economy. The results of this study can be used to guide policies related to the high-quality development of the urban digital economy. [ABSTRACT FROM AUTHOR]

Industry is widely valued as an important contributor to carbon emissions. Therefore, it is of great significance to analyze the industrial carbon emissions (ICE) in Guangdong, the strongest industrial province in China. We have adopted the carbon emission accounting model and standard deviational ellipse analysis model to analyze the temporal and spatial characteristics and evolution trends of the industry carbon emission amount and intensity in Guangdong from 1998 to 2013. The study results include: (1) Due to the rapid development of industry, Guangdong's ICE showed a steady growth trend; (2) The distribution characteristics of ICE were characterized by the trend of taking the Pearl River Delta (PRD) region as the center and gradually spreading to the surrounding areas. From the perspective of industrial sectors, it can be divided into steady growth type, fluctuant growth type, basically stable type, and decrease type; (3) The spatial pattern of the ICE in Guangdong is basically the same as that of the total industrial output value, that is, the southwest-northeast pattern. This work is helpful for China's carbon peak, especially for the formulation of industrial carbon peak policy and the sustainable development of the environment. [ABSTRACT FROM AUTHOR]

The arrival of the dual-carbon era indicates that it is imperative to reduce industrial carbon emissions with high carbon emissions. The implementation of carbon emission reduction will inevitably have an impact on industrial development. Three different carbon emission scenarios were established. Based on the average annual carbon emission growth rate of the past decade, it is set as a medium carbon scenario. According to the economic development stage and actual social development situation of each region, the corresponding annual average carbon emission growth rate is increased and decreased to set up high carbon and low carbon scenarios. The adjacent accumulation gray multivariate model for industries is established based on different scenarios. The results indicate that the carbon emission scenarios most suitable for industrial development in various regions are different. Heavy industrial provinces and cities such as Beijing, Tianjin and Hebei have the highest industrial output value under high carbon scenarios; Provinces and cities with high levels of industrialization, such as Jiangsu, Shanghai, Guangdong and Chongqing have the highest industrial output value under low-carbon scenarios. The industrial output value of Hebei is expected to grow the fastest under the high carbon scenario, reaching 2.15 trillion yuan by 2030. The industrial output value of Guangdong achieves the greatest improvement under low-carbon scenario. It is expected that the industrial output value of which will reach 6.86 trillion yuan by 2030, ranking first in the country. Therefore, the setting of carbon emission scenarios needs to be tailored to local conditions, increase technological innovation efforts, and further promote industrial development. [ABSTRACT FROM AUTHOR]

Investigating the linkage between financial development (FD) and carbon emissions is important for mitigating climate change. Nevertheless, there is a scarcity of studies investigating how carbon emissions decouple from FD. Here, we investigate the relationship between FD and carbon emissions by using the decoupling model based on cross-province data of China during 2000–2019. Then, we use the decomposition method to analyze the nine drivers of decoupling elasticity of FD and CO2 emissions. We found that China experienced weak decoupling and strong negative decoupling in most years. Only the finance develops at a very high level; the FD had spare capacities to promote the reduction in the carbon emissions. For example, several developed provinces (e.g., Tianjin, Zhejiang, Guangdong) realized strong decoupling after 2012. The reduction in energy intensity and the increase of foreign direct investment promoted the decoupling of FD from carbon emissions. During the financial recession period, developing a bank-based financial market helped the emissions reduction. Once financial crisis is overcome, developing a market-based financial market promoted the decoupling of FD from emissions. This is because that with the fast FD, the development of stock market contributed to emission reductions through technological improvement, while the bank loans inhibited the decoupling process through the expansion of capital-labor inputs. Overall, these results help in the assessment of the emissions impacts of FD and in addressing climate change problems. [ABSTRACT FROM AUTHOR]

China is pursuing aggressive action to reduce greenhouse gas emissions from its coal-dominated electric power system. Two key strategies are power market reform and carbon pricing. This paper investigates the combined effects of these two strategies in reducing CO 2 emissions from power system operations. We develop an economic dispatch model to simulate hourly power supply system operation in China Southern Power Grid in 2018 under fifteen carbon pricing scenarios; these reflect a wide range of policy ambition, from today's carbon prices to much higher ones that aim to instigate aggressive emission mitigation. Our results show that moderate carbon pricing alone is insufficient to effectively reduce CO 2 emissions without concurrent power market reform. With power market reform and as carbon prices increase, large coal units supplemented by energy storage witness higher use rates as they supplant smaller coal-fired generators, until a carbon price of 300 RMB/TCO 2 phases out coal use in favor of natural gas. Only at carbon prices higher than 300 RMB/TCO 2 do emissions begin to decrease appreciably. Geographic disparities emerge among the five provinces that comprise the Southern Power Grid, with Guangdong witnessing the most CO 2 emission reduction at high carbon prices, while emission reductions in other provinces are negligible. Carbon pricing also dramatically increases total power system costs, even at low carbon prices. Our results show the necessity of introducing power market reform and carbon pricing policies concurrently if the goal is to reduce CO 2 emissions, with the ultimate goal being a carbon price significantly greater than 300 RMB/TCO 2. Because both options will be necessary, our research maps a path to deep emission reductions in China Southern Power Grid for investors, analysts, and policy makers as discussions regarding both reforms accelerate. • We assess combined effects of carbon pricing and power market reform in China. • Carbon pricing cannot reduce CO 2 emissions without power market reform. • CO 2 reductions from carbon pricing are negligible until prices of 300 RMB/TCO 2. • Even with low carbon prices, power supply system cost increase appreciably. • Existing system can only achieve shallow decarbonization, even at 1000RMB/TCO 2. [ABSTRACT FROM AUTHOR]

This paper examines the risk spillover effect between the carbon market and the stock market in China and the role of corporate social responsibility (CSR) on this effect. Employing Beijing, Hubei, and Guangdong carbon markets, we apply time-domain and frequency-domain spillover approaches and find that during the Chinese stock market crisis in 2015, risk spillovers from the stock market to the carbon market were more pronounced. Additionally, CSR firms are more dominant as information transmitters than those non-CSR (NCSR) firms in the carbon market. However, plausibly, due to the infancy of carbon trading, our results show that the level of connectedness between the carbon market and the stock market in China is relatively low. [ABSTRACT FROM AUTHOR]

Carbon dioxide has a significant impact on global climate change due to its natural greenhouse effect. The objective and credible forecast of carbon emissions is very important for the government to formulate and implement the corresponding emission reduction targets. For controlling the growth of carbon emissions, Chinese government has put forward the low-carbon pilot policy and carbon trading policy. However, the existing grey models cannot measure the impact of policies and their interactions. In order to remedy the defect, a novel grey multivariable model based on dummy variables and their interactions is established. Two kinds of grey multivariable models and back propagation neural network model are chosen as comparison models to highlight that the introduction of dummy variables and their interactions plays an important part in improving the model performance. To verify the effectiveness, these four models are selected to simulate and predict the carbon emissions generated from primary energy consumption in Guangdong Province of China. The empirical results indicate that the mean absolute percentage errors of the novel model are 2.87% and 0.86%, respectively, which is significantly better than these three competing models. Finally, based on the outstanding performance of the novel model, it is chosen to forecast the fluctuating tendency of carbon emissions in the next 5 years. [ABSTRACT FROM AUTHOR]

An accounting model for the indirect household carbon emission (HCE) by using input–output analysis and a decomposition model of indirect HCE based on the structure decomposition method were established. The effects of sector aggregation on the indirect HCE and the decomposition result were investigated using Guangdong Province of China, as example, the economic development of which ranks first in China. The results showed that the indirect HCE of Guangdong accounted for about 51.7% of the total carbon emissions of Guangdong in 2017, showing a clear urban–rural dual structure. The per capita indirect HCE in Guangdong Province increased from 0.98 tCO2 in 1997 to 2.53 tCO2 in 2017, an increase in approximately 116%. Among the influencing factors of indirect HCE, the carbon emission factor was the most important influencing factor resulting in its reduction. The per capita consumption expenditure of residents was the most important influencing factor to promote growth of indirect HCE. The negative effects of the technological upgrade on indirect HCE can offset the positive effects of the population factor; however, they cannot offset the positive effects of the consumption factor. With the improvement of the living standard of residents, the proportion of their consumption in service industry with low-carbon emission intensity gradually increased. This helped to inhibit the excessive growth of the indirect HCE. Sector aggregation exerted a certain effect on the calculation results of indirect HCE, but the effects on the changing trend and decomposition results of indirect HCE were not significant. [ABSTRACT FROM AUTHOR]

As a new clean and sustainable energy that can reduce carbon emissions, offshore wind energy has received much more attention in recent years. In response to the complexity and flexibility issues in the decision-making process of offshore wind power investment, this paper proposes an investment decision framework based on a bidimensional binominal lattice that embeds deferral options, aiming to derive the appropriate development timing and investment potential for corporate investors in uncertain environment of market and technological development. And the investment model embedding revenue from carbon emission reduction highlights the advantages of clean energy. Furthermore, we creatively compare the policy effects of three subsidy schemes using the model as a policy analysis tool and use scenario analysis to simulate the impacts of dynamic adjustments in subsidies on investment decisions. For the case of an offshore wind power plant in Guangdong province, China, we find that the local government has to provide a subsidy of 0.11 CNY/kWh to allow the project to be profitable. In the absence of subsidies, however, it is preferable to defer the decision of investment with a wait-and-see attitude. Given the same total government financial input, we find that it will be more beneficial for the government to provide R&D subsidy than electricity tariff subsidy and operation subsidy in terms of long-term sustainable development. Our findings also demonstrate that considerations in the carbon trading market will also contribute to the development of offshore wind power. The conclusions of this article can provide valuable guidelines for corporate investors to adjust the appropriate development timing and for policymakers to optimize subsidy allocation, so as to promote the healthy development of offshore wind power and low-carbon transition. [ABSTRACT FROM AUTHOR]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

This paper proposes a new prediction method incorporating a set of measuring models applicable to the financing efficiency of China's carbon market to quantify such market's maturity, trading risk coefficient, and financing income. Our theoretical analysis indicates that the price of carbon emission quota essentially affects the financing efficiency of carbon market. While a robust safeguard measure to obtain a respectable income from carbon market financing is the long-term average quota price exceeding the initial quota price. Empirical findings derived from China's emissions trading scheme pilots reveal that these pilots can be divided into the growth-oriented market (i.e. Guangdong whose financing capacity is always significant), the balance-oriented market (i.e. Shanghai and Hubei whose quota pricing mechanisms and the financing level of carbon markets both maturely develop), and the risk-oriented market (i.e. Beijing whose quota price runs at a high-level with an intense financing income volatility). It is therefore achieved that carbon market's maturity level and quota price volatility are both robustly explicable for different financing effects among these pilots. Our key findings show that expanding the coverage of quota trading parties, stabilizing carbon price, and promoting carbon asset management help to improve the financing efficiency of carbon market. • The financing mechanism is analyzed with clarifying its main influence factors. • A stochastic process is used to build a price simulation equation that meets the operation law of China's carbon market. • An income model is built and thus evaluate market financing effect based on price expression and financing mechanism. • The financing effect of China's 8 pilot markets is measured, and thus 3 kinds of development modes are proposed. [ABSTRACT FROM AUTHOR]

Countries throughout the whole world, including China, are working together to curb the greenhouse effect, but the effects seem very limited in spite of the fact that various low-carbon development strategies have been adopted, particularly in industrial enterprises. Therefore, carbon emissions caused by the public should be taken seriously, and the public should be encouraged to engage in behavior that limits carbon emissions. Therefore, this article introduces a new incentive mechanism called the Carbon Generalized System of Preferences (CGSP), which was first introduced in Guangdong Province, China. It is believed that this new mechanism matches the role of leadership in Guangdong in the urbanization and economic development of China by addressing the small sources of greenhouse gases (GHGs) and by issuing carbon coins. Compared with Chinese Certified Emission Reduction (CCER), the development scope, management level, and novel criteria of CGSP are very different but relatively easy for the public to accept. The CGSP shows that the network platform, reduced carbon emissions, and urban pilots are all compatible with the goals of the nation and city, and they promote the CGSP in different ways. Because of its consistency with ecological civilization in China, the prospect of the CGSP is bright; however, there are some challenges, such as policy and economic factors, combined with pollution control. [ABSTRACT FROM AUTHOR]

The article applies a three-stage Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) pattern to examine the relationship between energy consumption and unfavorable CO2 emissions on green sustainable development, for the 11 cities of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2010–2016, by going through various violated factors and stochastic disturbance. Labor, capital and energy resource are chosen as input variables, while GDP and CO2 emission as output variables. During the three phases consisting of the SBM-DEA model (first stage and third stage) and SFA analysis (second stage), CO2 emission is considered as an unfavorable outcome, while stochastic statistical disturbances and external environmental influences are identified. The results show that the average efficiency of the GBA cities is 0.708, with only Shenzhen, Macao SAR and Hong Kong SAR having an efficiency of 1 during the whole study period. Based on the findings, suggestions are made for the GBA cities' sustainable development aspects. [ABSTRACT FROM AUTHOR]