Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors an...Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.展开更多
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that ...Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.展开更多
At the intersection of the“dual carbon”goal and the era of digital intelligence(DI),exploring the synergy between pollution and carbon reduction(SPCR)within the context of DI is important for promoting a comprehensi...At the intersection of the“dual carbon”goal and the era of digital intelligence(DI),exploring the synergy between pollution and carbon reduction(SPCR)within the context of DI is important for promoting a comprehensive green transformation of economic and social development.This study,based on urban panel data from 281 prefecture-level cities in China' Mainland from 2010 to 2020,developed a DI indicator system for these cities and employed a double machine learning algorithm for the first time to investigate the intrinsic mechanisms and incentivizing effects of DI on SPCR.The results showed that:①DI significantly promotes SPCR.②Mechanism tests demonstrated that DI can indirectly enhance SPCR by optimizing resource allocation and reinforcing government interventions.③Further analysis showed that the impact of DI on SPCR was more substantial in regions with lower levels of economic and environmental competition.Moreover,the SPCR driven by DI exhibited heterogeneity,characterized by stronger effects in“resource-based cities>non resource-based cities”and“non-capital economic zones>capital economic zones”.The conclusions of this study hold significant implications for fully harnessing the synergy between digitization and intelligence to empower SPCR.In addition,the findings are valuable for the government’s integrated promotion of the“dual carbon”goal and the“digital China”strategy.展开更多
The synergistic reduction of wastewater greenhouse gases(GHGs)and pollutants presents a critical environmental challenge.Understanding the synergistic efficiency and the factors that influence it is crucial for inform...The synergistic reduction of wastewater greenhouse gases(GHGs)and pollutants presents a critical environmental challenge.Understanding the synergistic efficiency and the factors that influence it is crucial for informed policy-making,but methods for assessing this efficiency are currently lacking.This study evaluates the synergistic efficiency in China from 2009 to 2019 using the elastic coefficient method,and assesses strict water policy impacts using double machine learning(DML).Results indicate that before 2015,China experiences synergistic increases,which shift to non-synergistic following the implementation of a strict water policy in 2015.Despite improved wastewater treatment rates,this policy paradoxically increases GHG emission intensity,leading to a“water-carbon”contradiction,especially in water-scarce,poorly enforced,and underdeveloped regions.The policy effect on GHG emission intensity is most influenced by wastewater pipeline infrastructure,followed by socioeconomic development,technological innovation,and industrial structure.Inefficiencies in GHG emission reductions are due to expanded wastewater treatment facilities and lower industrial energy efficiency.Conversely,higher salaries and technological advancements facilitate emission reductions.To achieve the synergy of effluent pollution and GHG reduction in the wastewater sector,provincial control priorities into four patterns are explored.This study provides guidance for low-carbon retrofitting of existing wastewater treatment plants and informs the design of effective water policies.展开更多
The global COVID-19 pandemic has severely impacted human health and socioeconomic development,posing an enormous public health challenge.Extensive research has been conducted into the relationship between environmenta...The global COVID-19 pandemic has severely impacted human health and socioeconomic development,posing an enormous public health challenge.Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19.However,numerous factors influence the development of pandemic outbreaks,and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19.Direct estimation of the role of environmental factors without removing the confounding effects will be biased.To overcome this critical problem,we developed a Double Machine Learning(DML)causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities.Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors.Environmental factors are not the dominant cause of widespread outbreaks in China in 2022.In addition,by further analyzing the causal effects of environmental factors,it was verified that there is significant heterogeneity in the role of environmental factors.The causal effect of environmental factors on COVID-19 changes with the regional environment.It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics,confounding factors must be handled carefully in order to obtain clean quantitative results.This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic,as well as a framework for more accurately quantifying the factors influencing the outbreak.展开更多
基金supported by the National Key R&D Program of China(No.2022YFA1005204l)。
文摘Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.
文摘Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.
基金funded by the General Program of the National Natural Science Foundation of China [Grant No.72473059]the Ministry of Education Humanities and Social Science Planning Fund Project [Grant No.23YJA790026]+1 种基金the Yunnan Province Basic Research Program General Project [Grant No.202401AT070393]the Innovation and Development Research Think Tank for Resource based Industries at Kunming University of Technology [Grant No.XXZK20-23006].
文摘At the intersection of the“dual carbon”goal and the era of digital intelligence(DI),exploring the synergy between pollution and carbon reduction(SPCR)within the context of DI is important for promoting a comprehensive green transformation of economic and social development.This study,based on urban panel data from 281 prefecture-level cities in China' Mainland from 2010 to 2020,developed a DI indicator system for these cities and employed a double machine learning algorithm for the first time to investigate the intrinsic mechanisms and incentivizing effects of DI on SPCR.The results showed that:①DI significantly promotes SPCR.②Mechanism tests demonstrated that DI can indirectly enhance SPCR by optimizing resource allocation and reinforcing government interventions.③Further analysis showed that the impact of DI on SPCR was more substantial in regions with lower levels of economic and environmental competition.Moreover,the SPCR driven by DI exhibited heterogeneity,characterized by stronger effects in“resource-based cities>non resource-based cities”and“non-capital economic zones>capital economic zones”.The conclusions of this study hold significant implications for fully harnessing the synergy between digitization and intelligence to empower SPCR.In addition,the findings are valuable for the government’s integrated promotion of the“dual carbon”goal and the“digital China”strategy.
基金the National Key Research and Development Program of China(No.2023YFC3210100)the National Natural Science Foundation of China(Nos.42177060 and 52470107)the Sichuan Science and Technology Program(Nos.2023NSFSC1949 and 2023ZHCG0024-LH)for the financial support.
文摘The synergistic reduction of wastewater greenhouse gases(GHGs)and pollutants presents a critical environmental challenge.Understanding the synergistic efficiency and the factors that influence it is crucial for informed policy-making,but methods for assessing this efficiency are currently lacking.This study evaluates the synergistic efficiency in China from 2009 to 2019 using the elastic coefficient method,and assesses strict water policy impacts using double machine learning(DML).Results indicate that before 2015,China experiences synergistic increases,which shift to non-synergistic following the implementation of a strict water policy in 2015.Despite improved wastewater treatment rates,this policy paradoxically increases GHG emission intensity,leading to a“water-carbon”contradiction,especially in water-scarce,poorly enforced,and underdeveloped regions.The policy effect on GHG emission intensity is most influenced by wastewater pipeline infrastructure,followed by socioeconomic development,technological innovation,and industrial structure.Inefficiencies in GHG emission reductions are due to expanded wastewater treatment facilities and lower industrial energy efficiency.Conversely,higher salaries and technological advancements facilitate emission reductions.To achieve the synergy of effluent pollution and GHG reduction in the wastewater sector,provincial control priorities into four patterns are explored.This study provides guidance for low-carbon retrofitting of existing wastewater treatment plants and informs the design of effective water policies.
基金supported by the Self-supporting Program of Guangzhou Laboratory(SRPG22-007)the National Key Research and Development Program of China(2023YFC3503400)the Gansu Province Intellectual Property Project under Grant(22ZSCQD02).
文摘The global COVID-19 pandemic has severely impacted human health and socioeconomic development,posing an enormous public health challenge.Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19.However,numerous factors influence the development of pandemic outbreaks,and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19.Direct estimation of the role of environmental factors without removing the confounding effects will be biased.To overcome this critical problem,we developed a Double Machine Learning(DML)causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities.Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors.Environmental factors are not the dominant cause of widespread outbreaks in China in 2022.In addition,by further analyzing the causal effects of environmental factors,it was verified that there is significant heterogeneity in the role of environmental factors.The causal effect of environmental factors on COVID-19 changes with the regional environment.It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics,confounding factors must be handled carefully in order to obtain clean quantitative results.This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic,as well as a framework for more accurately quantifying the factors influencing the outbreak.
