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As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

As sustainable development has become a trending consensus, urban ecological resilience is now a significant metric for assessing the extent of urban ecological advancement. In view of the critical roles for promoting coordinated regional development, this research evaluates the ecological resilience of 28 cities in city clusters in the middle reaches of the Yangtze River from 2009 to 2020 using the entropy weight method and analyzes the spatio-temporal evolution pattern of ecological resilience. This study uses the Moran index and the spatial Durbin model to explore the spatial correlation of ecological resilience as well as the influencing factors and presents the following conclusions. First, ecological resilience appears to be on an upward trend in time, spatially characterized by a transition from higher resilience in the east region to lower resilience in the west region. Second, spatial autocorrelation in ecological resilience exhibits a positive relationship. Most cities in Jiangxi Province are high-high agglomerative cities, while most cities in Hunan and Hubei Provinces are low-low homogeneous cities and high-low polarized cities, respectively. Third, given that population density, economic development, and financial development negatively affect ecological resilience, government green investment has a positive effect and positive spatial spillovers. Based on the findings, this paper offers targeted policy recommendations on how cities can improve urban ecological resilience. [Display omitted] • Enriching existing research by focusing on city clusters in the middle reaches of the Yangtze River. • Constructing an ecological resilience evaluation system based on the theory of resistance-adaptation-recovery. • Detailed analysis of the spatial and temporal evolution patterns of ecological resilience in city clusters. • Spatial factors are introduced to analyze the spatial correlation and influencing factors of ecological resilience. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality goals are proposed by the Chinese government, which aims to reach the highest level of CO 2 emissions by 2030 and achieve carbon neutrality by 2060. It is of great significance to accelerate green production and lead high-quality development. Energy carbon emissions account for 88% of total CO 2 emissions, while forest carbon sequestration account for 80% of China's terrestrial ecosystems. In this paper, we analyzed the spatial and temporal evolutions of industrial energy consumption carbon emissions and forest carbon sequestration of Hubei Province, then demonstrated the offsetting effect of forest carbon sequestration on the industrial CO 2 emissions. Our results documented energy carbon emissions and forest carbon sequestration in Hubei Province both increased from 2000 to 2020, with the growth rates of 4.9423 Mt/yr and 0.28015 Mt/yr. Forest carbon sequestration could offset energy carbon emissions before 2005, while the offsetting effect was weak due to the continuous increase in industrial energy consumption after 2005. Significant spatial heterogeneity was observed in both energy carbon emissions and forest carbon sequestration. Wuhan had the biggest carbon emission with annual average carbon emission of about 20.046 Mt, while Enshi had the biggest carbon sequestration with annual average forest carbon sequestration of about 14.411 Mt. Spatial autocorrelation between energy carbon emissions and forest carbon sequestration was significant in Hubei Province in the past two decades. Our findings provided evidence that local-scale forest carbon sequestration could offset carbon emissions caused by industrial energy consumption at a certain extent, helping to draw up the scientific energy-saving and emissions-reducing measures. [Display omitted] [ABSTRACT FROM AUTHOR]

The utilization of renewable energy sources to produce electricity is capable of relieving the pressure of limited natural resources and achieving the sustainability of future energy. This paper addresses a multi-period multi-feedstock multi-technology biomass-based power generation supply chain planning problem with parameter uncertainty, balancing economic, environmental, and social objectives simultaneously. However, the main challenges in optimizing this problem are correlated with multiple conflicting objectives and uncertain parameters. This study proposes a novel globalized robust goal programming model with goal constraints to balance three conflicting objectives using priority levels and characterize the uncertainty of unit emissions and social scores by inner–outer uncertainty sets. After transforming globalized robust environmental and social goal constraints into their equivalent forms, the tractable counterpart of the proposed model is obtained, which is mixed-integer linear programming (MILP). Finally, the effectiveness of the proposed model is demonstrated through a case study about the design of a sustainable biomass-based power generation supply chain (SBPSC) network in Hubei Province, China. Computational results reveal that by adjusting several parameters, environmental and social aspired goals consistently can achieve, whereas the economic objective is vulnerable to being influenced. Comparative studies with nominal goal programming model and robust goal programming model indicate that the proposed model is uncertainty-immunized and less conservative. Under investigated circumstances, the realized economic profit by the proposed model is approximately 42.5% higher than that of the robust goal programming model on average. • A pair of uncertainty sets is presented to characterize the parameter uncertainty. • Globalized robust environmental and social goal constraints are established. • Established goal constraints are transformed into their computationally tractable forms. • A novel globalized robust goal optimization method is proposed to balance three conflicting objectives using priority structure. • A case study shows the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of agricultural waste pollution has aroused wide concern around the world. An effective measure to solve this problem is to implement the compensation policy so as to encourage people to participate in agricultural waste recycling. This paper seeks to estimate households' willingness to accept compensation in terms of agricultural waste recycling adopting the contingent valuation method, and to calculate the compensation standard for households' participation in agricultural waste recycling by methods of non-parametric and parametric estimate. It aims to answer two questions: how much the compensation should be given for encouraging households to participate in agricultural waste recycling? Which factors would affect willingness to accept compensation value of households for participating in agricultural waste recycling? The results show that most households recognize the value of agricultural waste recycling and a certain amount of compensation which is about 1.08%–1.31% of average family annual income does encourage households to participate in agricultural waste recycling. Household's willingness to accept compensation and household's expected value of compensation are affected by personal or family characteristics (such as gender, household income) as well as the non-economic variables (such as environmental knowledge and environmental attitude). According to this research, reasonable compensation standard is the cornerstone of the effectiveness and efficiency of compensation policies. Household income inequality and capital allocation in terms of households' willingness to accept compensation should be taken into consideration when compensation policies about agricultural waste recycling are formulated. Environmental education for households is an effective measure to enhance their environmental consciousness, while raising the strength of government's waste management is turned out to be effective for improving the rural ecological environment. Conclusions of this study provide an important scientific basis for developing countries and regions to formulate compensation policies in terms of inspiring their agricultural waste recycling. [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]

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]

This paper investigates whether the China ETS policy has achieved carbon emission reduction at the expense of economic development. Moreover, we are interested in unmasking the role of the institutional factors adopted by the ETS pilots in their ETS effects. Using the province-level panel data during the 2008–2016 period. we employ the DID model to compare carbon emissions and economic development between the ETS and non-ETS regions and between the pre- and post-ETS periods. Some novel empirical findings emerge. First, compared with the non-ETS areas, the ETS policy has significantly reduced carbon emission in the ETS areas. This emission reduction has not come at the cost of economic development. Second, the ETS policy leads to a decline in carbon intensity and fossil fuel energy consumption relative to all energy types. Lastly, some heterogeneity across markets arise. The Beijing carbon market performs the best among all pilots in terms of achieving targets of carbon reductions, followed by the Hubei carbon market. [ABSTRACT FROM AUTHOR]

China launched seven emissions trading scheme (ETS) pilots in 2011, and a national ETS, at the end of 2017 in an attempt to reduce its greenhouse gas emissions (GHGs) and drive its extensive energy transition in a cost-effective way. The Hubei province pilot ETS has operated since 2014, and an analysis of its effectiveness can provide lessons for the nascent national system. This paper disaggregates Hubei's industrial carbon dioxide (CO 2) emissions from 2005 to 2018 into economic scale, economic structure, energy efficiency, and energy structure effects using the logarithmic mean Divisia index (LMDI). Difference-in-difference (DID) models then assess the implementation effectiveness of this ETS, and its impact on the main influencing factors of the LMDI. Ten cities each in the Hubei and Hunan provinces are selected as treatment and control groups, respectively, in the DID models. Results show that the change in industrial CO 2 emissions is mainly due to the economic scale and energy efficiency effects. After the ETS implementation, the energy structure effect has a limited impact on emissions, while the economic structure effect makes a negligible contribution. Moreover, the implementation of the ETS has had little demonstrable impact on industrial CO 2 emissions, gross domestic product (GDP), energy consumption intensity, and CO 2 emission intensity. This lacking of impact is caused by the GHG monitoring capacity deficits of the local Development and Reform Commission, and irrational quota allocations. Effective supervision and quota allocation should be developed to increase the impacts of the ETS. [ABSTRACT FROM AUTHOR]

To investigate the impacts of regulatory announcements on China's pilot carbon markets, as well as test the semi-strong form of market efficiency, a multivariate regression model with GARCH effects combined with multiple bilaterally modified dummy variables is established which allows us to investigate the potential overlapping influence from two timely adjacent events. The Shanghai, Guangdong and Hubei pilot carbon markets are taken as case studies. Based on the ADF test and the runs test, the results show that the China's pilot carbon markets have achieved the weak form of efficiency but not the semi-strong form of efficiency. With the regression model the results show that the release of the allowance allocation plan will affect the carbon price trend, and the allowance auction can only play an auxiliary role as its impact on the carbon price is not decisive. In addition, ubiquitous ex-ante impact proves that the information leakage exists in each pilot carbon market. The model established in this paper is suitable for analyzing the impacts of overlapping events in different markets, which can effectively assist decision makers to distinguish the effects caused by overlapping events. [ABSTRACT FROM AUTHOR]

China is the world's largest developing country and a carbon dioxide emitter. A functional carbon market can effectively reduce carbon emissions. This paper uses the Markov chain Monte Carlo-stochastic volatility model and the wavelet multi-resolution model to analyze the volatility of price returns and the dynamic characteristics of price fluctuations in the carbon pilot markets in Hubei, Shanghai, and Shenzhen. The price movements in these markets are compared to the emissions trading system of the European Union (EU-ETS). The results show that there is a volatility clustering in the price of carbon trading in Hubei, Shanghai, Shenzhen and the EU-ETS. China's carbon pilot markets have a deficiency in terms of volatility stability, as does the EU-ETS. From a long-term perspective, China's carbon market lacks a detailed development plan, which is vital because the construction of the market system is not yet optimal. From a short-term view, China's carbon market is not active and the participants' attitude toward risk is extremely sensitive. • MCMC-SV and wavelet multi-resolution model applied to carbon price volatility. • Three major Chinese markets are compared to the EU-ETS. • All markets evidence volatility clustering. • China's carbon market lacks a detailed development plan for the long term. • Short-term, China's carbon market must become more active and offer more products. [ABSTRACT FROM AUTHOR]

As an economic means to cope with climate change, carbon trading has become an important measure to reduce carbon emissions in China. It has been over seven years since the initiation of seven pilot carbon markets, but has the implementation achieved the goal of controlling carbon emissions in the region? Are the effects really resulted from the carbon market other than other energy-saving and emission reduction policies? Based on the provincial panel data of China from 2005 to 2016 this paper conducts an empirical study on the emission reduction effects of pilot projects using the Difference-in-differences model (DID). The results show that: (1) The implementation of carbon markets in Beijing, Shanghai and Hubei exhibited a significant inhibitory effect on local carbon emissions, but a promoting effect in Guangdong, and no significant effect was found in Tianjin; (2) In terms of time lag effect, no significant inhibiting effect on the local carbon emissions is found during the first year of opening of Shanghai carbon market but the effect grew significantly from 2015 to 2016; while for Hubei and Beijing, significant effect was shown from the first year of running. Being an experimental program to see whether the carbon trading policy would work in China or not and if it does, under what conditions and settings the policy becomes effective, the 7 pilot markets provide important experiences and lessons to the upcoming nationwide market in China. Based on the empirical result that the carbon trading system in Beijing, Shanghai and Hubei effectively reduced carbon emissions, it is suggested that the national carbon market should actively learn from the experiences of these three pilot markets particularly. [ABSTRACT FROM AUTHOR]

Carbon trading is one of the important mechanisms used to reduce carbon dioxide emissions. The increasing interest in the carbon trading market has heightened the need to decrease the prediction error of the carbon price. In this paper, a new hybrid model for carbon price forecasting is proposed, and the secondary decomposition algorithm is innovatively introduced into carbon price forecasting. First, time series data were decomposed into several intrinsic modal functions by empirical mode decomposition (EMD). Second, the first intrinsic mode function (IMF1) was further decomposed by variational mode decomposition (VMD). Then, the model input was determined by partial autocorrelation analysis (PACF). Finally, the back propagation (BP) neural network model optimized by genetic algorithm (GA) was utilized for prediction. In the empirical analysis of the Hubei market, the proposed model outperforms other comparative models. The mean absolute percentage error (MAPE), goodness of fit (R2) and root mean square error (RMSE) of the model are 1.7577%, 0.9929 and 0.5441, respectively. In the complementary cases of the Beijing and Shanghai carbon markets, the model also performs best. The results suggest that the proposed model is effective and robust and could predict carbon prices more accurately. Image 1 • The secondary decomposition algorithm is suitable for carbon price prediction. • The decomposition algorithm significantly improves the prediction accuracy. • Combination of empirical and variational mode decomposition is effective. • This method could be applied to different carbon markets. [ABSTRACT FROM AUTHOR]