Abstract
This study seeks to estimate the carbon implications of recent changes in China’s economic development patterns and role in global trade in the post-financial-crisis era. We utilised the ...latest socioeconomic datasets to compile China’s 2012 multiregional input-output (MRIO) table. Environmentally extended input-output analysis and structural decomposition analysis (SDA) were applied to investigate the driving forces behind changes in CO
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emissions embodied in China’s domestic and foreign trade from 2007 to 2012. Here we show that emission flow patterns have changed greatly in both domestic and foreign trade since the financial crisis. Some economically less developed regions, such as Southwest China, have shifted from being a net emission exporter to being a net emission importer. In terms of foreign trade, emissions embodied in China’s exports declined from 2007 to 2012 mainly due to changes in production structure and efficiency gains, while developing countries became the major destination of China’s export emissions.
For improving forecasting accuracy and trading performance, this paper proposes a new multi‐objective least squares support vector machine with mixture kernels to forecast asset prices. First, a ...mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data‐driven procedure. Second, a multi‐objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO2 assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi‐objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high‐level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.
The Chinese economy has been recovering slowly from the global financial crisis, but it cannot achieve the same rapid development of the pre-recession period. Instead, the country has entered a new ...phase of economic development-a 'new normal'. We use a structural decomposition analysis and environmental input-output analysis to estimate the determinants of China's carbon emission changes during 2005-2012. China's imports are linked to a global multi-regional input-output model based on the Global Trade and Analysis Project database to calculate the embodied CO2 emissions in imports. We find that the global financial crisis has affected the drivers of China's carbon emission growth. From 2007 to 2010, the CO2 emissions induced by China's exports dropped, whereas emissions induced by capital formation grew rapidly. In the 'new normal', the strongest factors that offset CO2 emissions have shifted from efficiency gains to structural upgrading. Efficiency was the strongest factor offsetting China's CO2 emissions before 2010 but drove a 1.4% increase in emissions in the period 2010-2012. By contrast, production structure and consumption patterns caused a 2.6% and 1.3% decrease, respectively, in China's carbon emissions from 2010 to 2012. In addition, China tends to shift gradually from an investment to a consumption-driven economy. The proportion of CO2 emissions induced by consumption had a declining trend before 2010 but grew from 28.6%-29.1% during 2010-2012.
Major cities in eight economy-geography regions of China. Display omitted
•Industrial energy and emissions efficiency were evaluated for China’s major cities.•Shadow prices of CO2 emissions were ...estimated for China’s major cities.•Efficiency increase potentials on energy utilization and CO2 emissions are 19% and 17%.•N-shaped EKC exists between levels of CO2 emissions efficiency and income.•Average industrial CO2 emissions abatement cost for China’s major cities is 45 US$.
Evaluating the energy and emissions efficiency, measuring the energy saving and emissions reduction potential, and estimating the carbon price in China at the regional level are considered a crucial way to identify the regional efficiency levels and efficiency promotion potentials, as well as to explore the marginal abatement costs of carbon emissions in China. This study applies a newly developed Data Envelopment Analysis (DEA) based method to evaluate the regional energy and emissions efficiencies and the energy saving and emissions reduction potentials of the industrial sector of 30 Chinese major cities during 2006–2010. In addition, the CO2 shadow prices, i.e., the marginal abatement costs of CO2 emissions from industrial sector of these cities are estimated during the same period. The main findings are: (i) The coast area cities have the highest total factor industrial energy and emissions efficiency, but efficiency of the west area cities are lowest, and there is statistically significant efficiency difference between these cities. (ii) Economically well-developed cities evidence higher efficiency, and there is still obviously unbalanced and inequitable growth in the nationwide industrial development of China. (iii) Fortunately, the energy utilization and CO2 emissions efficiency gaps among different Chinese cities were decreasing since 2006, and the problem of inequitable nationwide development has started to mitigate. (iv) The Chinese major cities could have, on average, an approximately 19% or 17% efficiency increase on energy utilization or CO2 emissions during 2006–2010. (v) Promoting the industrial energy utilization efficiency is comparatively more crucial for Chinese cities at the current stage, and the efficiency promotion burdens on the west area cities are the heaviest among all Chinese cities. (vi) An N-shaped Environmental Kuznets Curve (EKC) exists between the level of industrial CO2 emissions efficiency and income, and the inflection point the EKC is located between 12,052 and 12,341 US$ of GDP per capita, indicating that an accelerated CO2 emissions efficiency increase will accrue when this income level is reached. (vii) In 2010, the industrial total energy saving and CO2 emissions reduction potentials for Chinese major cities were 41 million tce and 143 million tCO2, respectively. (viii) The average industrial CO2 emissions abatement cost for Chinese major cities is 45 US$ during 2006–2010, and the existence of large gap on CO2 shadow prices between different Chinese regions provide a necessity and possibility for establishing a regional carbon emissions trading system in China.
The impacts of urbanization on carbon emissions have attracted widespread attention for a long time. Quantitative research on the impacts is of great significance for formulating carbon reduction ...policy. Based on the dynamic panel autoregressive distribution lag (ARDL) model, we systematically study the general and heterogeneous long-run equilibrium relationships, short-run dynamic relationships, impact mechanism and lag effect between urbanization and three carbon emission dimensions in OECD high-income countries during long period. The main empirical results indicate that developed countries tend to have the same negative impacts of urbanization on carbon emissions, although there are differences in the endowments of different countries. The impact is relatively weak, for each percentage point increase in urbanization rate, CO2 emissions per capita decrease by 0.015%, total CO2 emissions decrease by 0.012%, and CO2 emission intensity decrease by 0.009%. All member countries have achieved the decoupling of urbanization and carbon emissions. Urbanization affects carbon emissions by affecting economic growth, energy efficiency, and final energy consumption structure. This paper further reveals the multi-level impacts of urbanization on carbon emissions and provides policy implications of achieving carbon reduction through urbanization's agglomeration effect for government decision makers.
•Estimate the impact of urbanization on carbon emissions based on panel ARDL model.•Analyze general & heterogeneous long-, short-run relationship and impact mechanism.•Urbanization decreases carbon emissions but the impact is weak in OECD countries.•Developed economies have achieved the decoupling of urbanization and CO2 emissions.•Promote urbanization process and exert its scale effect to reduce carbon emissions.
•A Data envelopment analysis based materials balance approach is proposed.•Materials balance principle guarantees satisfaction of laws of thermodynamics.•Environmental efficiency of China's thermal ...power industry is measured.•Efficiency is decomposed into technical efficiency and input allocative efficiency.•End-of-pipe pollutant abatement activities are included in measurement.
Appropriate measurement of environmental and emission abatement efficiency is crucial for assisting policy making in line with constructing a more sustainable society. The majority of traditional approaches for environmental efficiency measures take pollutant emissions as either undesirable outputs or environmentally determined inputs which suffer a limitation of not satisfying the physical laws that regulate the operation of economic and environmental process. In this study, we propose a DEA based approach which is combined with the materials balance principle (MBP) that accounts for laws of thermodynamics to jointly evaluate environmental and abatement efficiency. This approach is along the line of weak G-disposability based modelling but is an extension to existing models that in our approach the identification of possible adjustments on polluting mass bound in inputs and outputs, and potential adjustments on abatement of pollutants are all included. The overall environmental efficiency measured by this approach is decomposed into the measures of technical efficiency, polluting inputs allocative efficiency, and polluting and non-polluting inputs allocative efficiency with the emphasizing of incorporating pollutant abatement activities. Accordingly, new measures of abatement efficiency are proposed which help to identify the pollutant abatement potential that can be achieved from end-of-pipe abatement technology promotion associated with polluting input quality promotion and input resources reallocation. Furthermore, several global Malmquist productivity indices for identifying the changes on environmental and abatement efficiency are proposed. This approach is applied to China's thermal power industry and some empirical results verifying the necessity of introducing the MBP are obtained.
Copper oxide‐based materials effectively electrocatalyze carbon dioxide reduction (CO2RR). To comprehend their role and achieve high CO2RR activity, Cu+ in copper oxides must be stabilized. As an ...electrocatalyst, Cu2O nanoparticles were decorated with hexagonal boron nitride (h‐BN) nanosheets to stabilize Cu+. The C2H4/CO ratio increased 1.62‐fold in the CO2RR with Cu2O−BN compared to that with Cu2O. Experimental and theoretical studies confirmed strong electronic interactions between the two components in Cu2O−BN, which strengthens the Cu−O bonds. Electrophilic h‐BN receives partial electron density from Cu2O, protecting the Cu−O bonds from electron attack during the CO2RR and stabilizing the Cu+ species during long‐term electrolysis. The well‐retained Cu+ species enhanced the C2 product selectivity and improved the stability of Cu2O−BN. This work offers new insight into the metal‐valence‐state‐dependent selectivity of catalysts, enabling the design of advanced catalysts.
Strong electronic interactions between hexagonal boron nitride (h‐BN) and Cu2O protect the Cu−O bonds against electron attack through the transfer of accumulated electrons from Cu2O to h‐BN. This effect stabilizes the active Cu+ species during CO2 electroreduction.
Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load ...forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models.
•Advanced computational model aims at developing accurate solution techniques.•Electricity load is decomposed into regular components by improved EMD.•Different features associated with electricity load can be captured by the proposed model.•Hybrid model composed with different models performs well than singe model.
Renewable energy is an efficient tool to support China's endeavors to keep energy independence and mitigate climate change. This paper applies a Divisia index approach to investigate the factors ...governing renewable energy development in China, including the supply mix, energy security, carbon emission, and to forecast these requirements for the year 2020 and 2030. Grey relational model is employed to verify the relationships between renewable energy and its drivers. The results of the forecasts reveal the challenges of long-term deployment for renewable energy technologies. Other results show that during the research period, energy security makes a major contribution to renewable energy development. Energy security and substitution rate have relatively closer relationships with new and total renewable energy consumption respectively than that of other factors. Scenario analyses suggest that strong and continuing renewable energy policies will be helpful to achieve sustainable energy development in China and a strong synergy between renewable energy and energy security would emerge in the future.
•Contributions of different factors to renewable energy consumption are examined by Divisia decomposition approach.•Scenario analysis is employed to forecast future requirements of these factors to renewable energy utilization.•The relationships between renewable energy development and China's energy security reveal the future synergy between them.•Energy transition to renewables may promote future renewable penetration other than the past negative contribution.
Objective To assess the associations of the Assessment of Spondyloarthritis International Society Health Index (ASAS HI) with gender and other factors in patients with ankylosing spondylitis (AS). ...Methods From November 2017 to October 2018, we measured the Ankylosing Spondylitis Disease Activity Score (ASDAS), the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) and the ASAS HI score for AS patients at the Taichung Veterans General Hospital. After adjusting for disease activity (ASDAS-erythrocyte sedimentation rate ESR, ASDAS- C-reactive protein CRP, BASDAI+ESR or BASDAI+CRP), mSASSS and other potential confounders including medications, comorbidities, and laboratory data, any associations between gender and the sum score of ASDAS HI were assessed using multiple linear regression analysis, as well as any associations between gender and an ASAS HI score >5 using multivariable logistic regression analysis. Results A total of 307 AS patients (62 20.2% females, mean age 46.4 years S.D. 13.3, mean symptom duration 20.6 years S.D. 12.1) were included. Multiple linear regression analysis showed that the male gender was significantly associated with a lower ASAS HI (B = -1. 91, 95% confidence interval CI, -2.82--1.00, p 5 than females (odds ratio = 0.15, 95% CI, 0.07-0.36, p 0.001). Disease activity measures, including ASDAS-ESR, ASDAS-CRP and BASDAI, had positive correlations with ASAS HI. Conclusion This single-center, cross-sectional study revealed that a higher ASAS HI score was significantly associated with female gender and higher disease activity measures.
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