Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analys...Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.展开更多
Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission perf...Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.展开更多
The large amount of carbon emissions generated by buildings during their life cycle greatly impacts the environment and poses a considerable challenge to China’s carbon reduction efforts.The building design phase has...The large amount of carbon emissions generated by buildings during their life cycle greatly impacts the environment and poses a considerable challenge to China’s carbon reduction efforts.The building design phase has the most significant potential to reduce building life-cycle carbon emissions(LCCO_(2)).However,the lack of detailed inventory data at the design stage makes calculating a building’s LCCO_(2) very difficult and complex.Therefore,accurate prediction of building LCCO_(2) at the design stage using relevant design factors is essential to reduce carbon emissions.This paper proposes an ensemble learning algorithm combining Bayesian optimization and extreme gradient boosting(BO-XGBoost)to predict LCCO_(2) accurately in residential buildings.First,this study collected and calculated the LCCO_(2) of 121 residential buildings in Chengdu,China.Second,a carbon emission prediction model was developed using XGBoost based on 15 design factors,and hyperparameter optimization was performed using the BO algorithm.Finally,the model performance was evaluated using two evaluation metrics,coefficient of determination(R2)and root mean square error(RMSE),and the prediction performance of other models was compared with that of the BO-XGBoost model.The results show that the RMSE of the proposed BO-XGBoost for predicting LCCO_(2) in residential buildings is at least 40%lower compared to other models.The method adopted in this study can help designers accurately predict building LCCO_(2) at the early design stage and provide methodological support for similar studies in the future.展开更多
我国已宣布力争2030年前二氧化碳排放达到峰值,为确保河北省能够保质保量完成碳达峰目标,采用联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change, IPCC)排放因子法测算河北省2005-2021年化石能源消费碳排放量...我国已宣布力争2030年前二氧化碳排放达到峰值,为确保河北省能够保质保量完成碳达峰目标,采用联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change, IPCC)排放因子法测算河北省2005-2021年化石能源消费碳排放量,选取人口、人均GDP、城镇化率、产业结构、能源强度和能源结构6个因素,构建了河北省碳排放人口、财富和技术影响(stochastic impacts by regression on population, affluence, and technology, STIRPAT)预测模型,通过构建河北省碳排放情景,对河北2022-2040年碳排放量进行了预测。结果表明在基准情景和经济发展情景下,河北省碳排放趋势是持续上升的,未出现达峰点;产业转型、绿色发展和目标导向情景下出现了峰值点,其中目标导向情景在2029年达峰,绿色发展情景在2030年达峰,碳达峰量分别为81 626.658万吨二氧化碳和86 018.255万吨二氧化碳,产业转型情景在2035年达峰,碳达峰量为85 214.349万吨二氧化碳。按照目前情景发展下河北省难以在2030年实现碳达峰,为保质保量完成达峰目标,需要以能源绿色低碳发展为关键手段,同时以科技和制度创新为动力,调整优化产业结构和能源结构。展开更多
基金Project(50838009) supported by the National Natural Science Foundation of ChinaProjects(2006BAJ02A09,2006BAJ01A13-2) supported by the National Key Technologies R & D Program of China
文摘Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.
基金Fundamental Research Funds for the Central UniversitiesNo.19lgzd09+2 种基金Guangdong Special Support ProgramPearl River S&T Nova Program of GuangzhouNo.201806010187
文摘Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.
基金supported by the National Natural Science Foundation of China (No.52078442)the China Scholarship Council.
文摘The large amount of carbon emissions generated by buildings during their life cycle greatly impacts the environment and poses a considerable challenge to China’s carbon reduction efforts.The building design phase has the most significant potential to reduce building life-cycle carbon emissions(LCCO_(2)).However,the lack of detailed inventory data at the design stage makes calculating a building’s LCCO_(2) very difficult and complex.Therefore,accurate prediction of building LCCO_(2) at the design stage using relevant design factors is essential to reduce carbon emissions.This paper proposes an ensemble learning algorithm combining Bayesian optimization and extreme gradient boosting(BO-XGBoost)to predict LCCO_(2) accurately in residential buildings.First,this study collected and calculated the LCCO_(2) of 121 residential buildings in Chengdu,China.Second,a carbon emission prediction model was developed using XGBoost based on 15 design factors,and hyperparameter optimization was performed using the BO algorithm.Finally,the model performance was evaluated using two evaluation metrics,coefficient of determination(R2)and root mean square error(RMSE),and the prediction performance of other models was compared with that of the BO-XGBoost model.The results show that the RMSE of the proposed BO-XGBoost for predicting LCCO_(2) in residential buildings is at least 40%lower compared to other models.The method adopted in this study can help designers accurately predict building LCCO_(2) at the early design stage and provide methodological support for similar studies in the future.
文摘我国已宣布力争2030年前二氧化碳排放达到峰值,为确保河北省能够保质保量完成碳达峰目标,采用联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change, IPCC)排放因子法测算河北省2005-2021年化石能源消费碳排放量,选取人口、人均GDP、城镇化率、产业结构、能源强度和能源结构6个因素,构建了河北省碳排放人口、财富和技术影响(stochastic impacts by regression on population, affluence, and technology, STIRPAT)预测模型,通过构建河北省碳排放情景,对河北2022-2040年碳排放量进行了预测。结果表明在基准情景和经济发展情景下,河北省碳排放趋势是持续上升的,未出现达峰点;产业转型、绿色发展和目标导向情景下出现了峰值点,其中目标导向情景在2029年达峰,绿色发展情景在2030年达峰,碳达峰量分别为81 626.658万吨二氧化碳和86 018.255万吨二氧化碳,产业转型情景在2035年达峰,碳达峰量为85 214.349万吨二氧化碳。按照目前情景发展下河北省难以在2030年实现碳达峰,为保质保量完成达峰目标,需要以能源绿色低碳发展为关键手段,同时以科技和制度创新为动力,调整优化产业结构和能源结构。
