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The green transformation of heavy-polluting enterprises under the carbon peaking and carbon neutrality goals needs the escort of command environmental regulation. This paper takes the first round of Central Environmental Protection Inspection as a natural experiment, reflecting the degree of green transformation through the environmental, social, and governance (ESG) level of enterprises. Taking Chinese A-share listed companies in Shanghai and Shenzhen from 2010 to 2022 as the research object, a dual machine learning model is used to determine whether this system can improve the ESG level of enterprises and promote their green transformation. Using a dual machine learning model, it is found that the system can enhance the ESG level of enterprises and promote their green transformation. Mechanism analyses show that the system promotes green transformation by enhancing green innovation capability, safeguarding the rights and interests of upstream and downstream supply chain actors, and enhancing governance diligence, thus encouraging enterprises to assume environmental, social, and governance responsibilities. It is also found that at the macro-policy level, with the migration of the inspection area to the west and the strengthening of local environmental governance, and at the micro-firm level, the higher the degree of digitization and the larger the size of assets, the deeper the system effect on the green transformation of heavy-polluting enterprises. It is further found that the spillover effect of green transformation within industries strengthens the positive effect of the Central Environmental Protection Inspection on the green transformation of enterprises compared to the interindustry. [ABSTRACT FROM AUTHOR]

The listed A-share manufacturing enterprises in China from 2010 to 2019 serve as the research samples in this study. Moreover, the influence mechanism and effect of digital transformation of enterprises on technology innovation are studied at the micro level. The digital transformation of enterprises can facilitate technology innovation, and this effect continues to be significant after a series of endogenous and robustness tests are performed, as indicated by the results of this study. For mechanism, digital transformation is capable of indirectly affecting technology innovation via three channels, including reducing costs, elevating the efficiency of human capital, and deepening R&D collaboration among enterprises. In-depth analysis suggests that the effect of digital transformation on technology innovation exhibits certain heterogeneity in the development level of regional digital economy and the capital intensity of enterprises. Furthermore, enterprises in areas with higher development level of digital economy and those exhibiting higher capital intensity better enjoy the stimulating effect of digital transformation on technology innovation. [ABSTRACT FROM AUTHOR]

Stimulating the endogenous motivation of the impoverished individual is crucial to prevent the reoccurrence of widespread poverty, consolidate efforts of poverty eradication, and achieve rural revitalization. Considering whether villages receive tourism-targeted assistance as a quasi-natural experiment, using the Double Machine Learning (DML) method and six periods of balanced panel data from 392 impoverished households, the research analyzes the impact of tourism-targeted assistance on endogenous motivation. Findings indicate that, through promoting rural industry development and facilitating local employment, tourism-targeted assistance stimulate the endogenous motivation of the impoverished individuals efficiently. This effect is more pronounced in impoverished households with affluent labor force, high level of education and those engaged in tourism. Consequently, future efforts for rural revitalization should refine the strategy of targeted assistance based on the resource and demand of the rural development. [ABSTRACT FROM AUTHOR]

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.

[Objective] The establishment of the national big data comprehensive pilot zone is an important method for promoting the digitization and scientific advancement of environmental protection. This paper aims to study how the construction of pilot zones affects environmental pollution, providing a theoretical basis for fully leveraging the policy effectiveness of the pilot zones and promoting green development. [Methods] Using panel data from 274 Chinese cities from 2010 to 2020, we calculated a comprehensive environmental pollution index for each city. We then treated the construction of the “national big data comprehensive pilot zone” (hereinafter referred to as the pilot zone) as a quasi-natural experiment and employed double machine learning to investigate the impact and mechanisms through which the pilot zone affected urban environmental pollution, as well as its heterogeneous characteristics. [Results] (1) The pilot zone significantly reduce environmental pollution in its cities. This conclusion remained valid after a series of robustness tests, including winsorizing the sample, changing the sample splitting ratio, and altering the machine learning algorithms used. (2) The pilot zone can indirectly inhibit urban environmental pollution through three pathways: green technological innovation, human capital input, and environmental regulation. (3) In terms of heterogeneity, the pilot zone had a significant positive effect on reducing urban environmental pollution in non-resource-based cities, central cities and cities with higher pollution levels. [Conclusion] Therefore, we recommend to strengthen top-level design and fully utilize the pilot zone to promote environmental protection. Strategies should be tailored to local conditions to implement differentiated development plans. Additionally, the key roles of green technological innovation, human capital, and environmental regulations should be leveraged to alleviate environmental burdens.

Abstract The transformation and upgrading of China’s manufacturing industry is supported by smart and green manufacturing, which have great potential to empower the nation’s green development. This study examines the impact of the Made in China 2025 industrial policy on urban green economic growth. This study applies the super-slacks-based measure model to measure cities’ green economic growth, using the double machine learning model, which overcomes the limitations of the linear setting of traditional causal inference models and maintains estimation accuracy under high-dimensional control variables, to conduct an empirical analysis based on panel data of 281 Chinese cities from 2006 to 2021. The results reveal that the Made in China 2025 strategy significantly drives urban green economic growth, and this finding holds after a series of robustness tests. A mechanism analysis indicates that the Made in China 2025 strategy promotes green economic growth through green technology progress, optimizing energy consumption structure, upgrading industrial structure, and strengthening environmental supervision. In addition, the policy has a stronger driving effect for cities with high manufacturing concentration, industrial intelligence, and digital finance development. This study provides valuable theoretical insights and policy implications for government planning to promote high-quality development through industrial policy.

This article considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data. The estimator is available in the causalweight package for the statistical software R. [ABSTRACT FROM AUTHOR]

The long-standing, unrestrained utilization of energy resources by China's manufacturing sector has created irreversible obstacles to regional sustainable development. Consequently, the Chinese government has implemented a water resource tax policy in certain regions, with the aim of compelling manufacturing enterprises to adopt green and energy-saving innovations. This study used panel data from Chinese manufacturing companies listed on the A-share market from 2009 to 2020 and employed a double machine learning model to explore whether the water resource fee-to-tax reform can compel enterprises to enhance their tripartite green energy-saving innovation drive. These innovations consist of vision-driven and mission-driven green energy-saving technological innovations and green management energy-saving innovations. Following a quasi-natural experiment, our findings revealed the following: (1) The water resource fee-to-tax policy promoted the internal coupling coordination of the triple-driven system. (2) The policy compelled progress in mission-driven green energy-saving technological innovations and green energy-saving management innovations but hindered vision-driven green energy-saving technological innovations. (3) Within the internal systems of manufacturing enterprises, green energy-saving management innovations play a positive mediating role between the water resource fee-to-tax policy and the mission-driven green energy-saving technology innovation subsystem, but they lack a similar positive mediating mechanism for the vision-driven green energy-saving technology innovation subsystem. (4) The counterfactual framework verified that the mechanistic pathway "water resource fee-to-tax → green energy-saving management innovation → mission-driven/vision-driven green energy-saving technological innovation" could be further extended to other manufacturing enterprises not currently under policy compulsion. (5) In the interaction system between manufacturing enterprises and external markets, the development of marketization and financial technology positively regulated the promoting effect of the water resource fee-to-tax policy on mission-driven green energy-saving technological innovations and green energy-saving management innovations, but it did not have a similar effect on vision-driven green energy-saving technological innovations. [ABSTRACT FROM AUTHOR]

As the world's largest emerging market country, not only has China faced the contradiction between its huge population size and per capita energy scarcity for a long time, but the rigid constraints brought by energy poverty have also plagued the lives and production of Chinese residents. Based on panel data from 30 provinces (except Tibet) in mainland China from 2009 to 2021, this study employs double machine learning and spatial difference-in-difference for causal inference to explore the impact of a medium- to long-term regional innovation pilot policy in China—the new policy for innovative transformation in regional industrial chains—on energy poverty alleviation. This study also introduces China's conversion of new and old kinetic energy into this quasi-natural experiment. This study presents the following findings: (1) The new policy for innovative transformation in regional industrial chains and the concept of the conversion of new and old kinetic energy can both significantly promote energy poverty alleviation. (2) The mechanism pathway of "the new policy for innovative transformation in regional industrial chains → the conversion of new and old kinetic energy → the energy poverty alleviation in heating/household electricity/transportation segments" has proved to be an effective practice in China. (3) Based on the spatial double difference model, the spatial direct effect of the new regional industrial chain innovation and change policy on energy poverty alleviation is significantly positive, while the spatial direct effect and spatial spillover effect of the new and old kinetic energy transformation on energy poverty alleviation are both significantly positive. (4) Based on the counterfactual framework analysis, in addition to the causal mediating mechanism of the demand-side conversion of new and old kinetic energy being impeded, both the supply-side and the structural-side conversion of new and old kinetic energy are able to play a significant positive causal mediating role in both the treatment and control groups. [ABSTRACT FROM AUTHOR]

China is facing a serious land resource mismatch problem, which will profoundly affect the acceleration of economic growth and technological innovation. Reform of the industrial land allocation system can solve the mismatch of land resources, and that also has an important impact on the promotion of economic and technological development. This paper selects the data of Chinese A-share listed companies in Shanghai and Shenzhen from 2007 to 2020 as the research sample, constructs a double machine learning model, and empirically investigates the impact of a new industrial land use policy on firms' green innovation behavior. The study shows that: (1) the new industrial land use policy significantly promotes firms' substantive and strategic green technological innovation, and the effect on substantive green technological innovation is greater than that on strategic green technological innovation. (2) The enhancement of R&D investment sustainability and the "talent pool" effect are important mechanisms through which the new industrial land use policy influences firms' substantive and strategic green technological innovation. Meanwhile, the new industrial land use policy is conducive to firms' green coinnovation. (3) There is heterogeneity in the effect of the new industrial land use policy, which can significantly promote green technological innovation of firms in the eastern region, while it does not play a significant role in the green innovation behavior of firms in the central and western regions. The above research results enrich the research in the field of industrial land and innovation, help to understand more comprehensively the mechanism of new industrial land affecting firms' green technological innovation, and provide policy insights for strengthening the application of industrial land allocation reform in firms' green innovation. [ABSTRACT FROM AUTHOR]

The transformation and upgrading of China's manufacturing industry is supported by smart and green manufacturing, which have great potential to empower the nation's green development. This study examines the impact of the Made in China 2025 industrial policy on urban green economic growth. This study applies the super-slacks-based measure model to measure cities' green economic growth, using the double machine learning model, which overcomes the limitations of the linear setting of traditional causal inference models and maintains estimation accuracy under high-dimensional control variables, to conduct an empirical analysis based on panel data of 281 Chinese cities from 2006 to 2021. The results reveal that the Made in China 2025 strategy significantly drives urban green economic growth, and this finding holds after a series of robustness tests. A mechanism analysis indicates that the Made in China 2025 strategy promotes green economic growth through green technology progress, optimizing energy consumption structure, upgrading industrial structure, and strengthening environmental supervision. In addition, the policy has a stronger driving effect for cities with high manufacturing concentration, industrial intelligence, and digital finance development. This study provides valuable theoretical insights and policy implications for government planning to promote high-quality development through industrial policy. [ABSTRACT FROM AUTHOR]

This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China's impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path "innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy" has significantly positive direct and indirect effects in both the treatment group and the control group. However, "innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector's impossible triangle coupling coordination degree" and "innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector's impossible triangle coupling coordination degree" have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group. [ABSTRACT FROM AUTHOR]

This paper estimates the dynamic effect of vaccination on mortality from COVID-19 using weekly data from 26 European Union countries during 2021. Our analysis relies on the double machine learning method to control for multiple confounders, including nonpharmaceutical interventions, climate variables, mobility factors, variants of concern, country- and week-specific shocks. In our baseline specification, we show that a 10 percentage point increase in cumulative doses per hundred inhabitants averts 5.08 COVID-19 deaths per million inhabitants at the eight-week horizon and 26.41 deaths in the eight-week time window considered. The average reduction in mortality in this window is close to 50%. Further estimates reveal that the effect of doses administered to adults aged 18–59 does not statistically differ from that of doses received by people aged 60 and over. Finally, vaccine-specific estimates document that mRNA-1273 (Moderna) and Vaxzevria (AstraZeneca) are more cost-effective in saving lives than Comirnaty (Pfizer), while we are unable to demonstrate any effect of Ad26.COV2.S (Johnson & Johnson). [ABSTRACT FROM AUTHOR]

The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data's suppressive effect on PM2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies. [ABSTRACT FROM AUTHOR]

This article investigates the causal effect of farm participation in two Austrian agri‐environmental schemes (AES), Immergrün (ground cover) and Zwischenfrucht (catch cropping), on fertilizer and plant protection expenditures in the 2014 programming period. Combining European Farm Accountancy Data Network data with information on scheme participation from administrative control data offers identifying farm participation in specific schemes targeted at reducing input intensity. Given the overall small sample, we maximized the utilizable sample size by combining difference‐in‐difference and kernel matching with automated bandwidth selection. To address the remaining post‐matching covariate imbalances, we used double machine learning (DML) techniques for a guided selection of potential confounding covariates. Our results suggest that, given the available sample, we cannot substantiate moderate effects of AES participation, and that guided covariate selection by DML offers no gain over non‐guided covariate selection for the small sample. Our results underline the need to increase the number of farms and the duration in available farm panels to substantiate future counterfactual‐based evaluations of policy. [ABSTRACT FROM AUTHOR]

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as n -consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event. [ABSTRACT FROM AUTHOR]

Geographical indications (GIs) mitigate information asymmetry in agri-food transactions by providing consumers with origin and quality information. This paper explores the impact of GIs on rural development in China by examining agricultural output and farmers' income. Utilizing a large county-level dataset and comprehensive official GI information, this study estimates the impact of GIs on agricultural output and rural income using panel-fixed-effects models. The results reveal that GIs significantly boost agricultural added value and rural per capita disposable income. A series of methods, including difference-in-differences, propensity score matching with difference-in-differences, and double machine learning combined with difference-in-differences using random forests verify the robustness of the results. Moreover, by categorizing GIs based on product types, the analysis reveals heterogeneous effects of different GI categories on agricultural growth and income gains for farmers. The research findings in this paper offer valuable insights to inform policymaking aimed at advancing rural development, raising farmers' incomes, and promoting sustainable agri-food supply chains. [ABSTRACT FROM AUTHOR]

Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed‐effects model for repeated measurements. Using machine learning algorithms allows us to incorporate complex interaction structures, nonsmooth terms, and high‐dimensional variables. The linear variables and the response are adjusted nonparametrically for the nonlinear variables, and these adjusted variables satisfy a linear mixed‐effects model in which the linear coefficient can be estimated with standard linear mixed‐effects methods. We prove that the estimated fixed effects coefficient converges at the parametric rate, is asymptotically Gaussian distributed, and semiparametrically efficient. Two simulation studies demonstrate that our method outperforms a penalized regression spline approach in terms of coverage. We also illustrate our proposed approach on a longitudinal dataset with HIV‐infected individuals. Software code for our method is available in the R‐package dmlalg. [ABSTRACT FROM AUTHOR]

When solving the performance‐based earthquake engineering (PBEE) integral, the engineering demand parameters (EDPs) are assumed to be independent of the "upstream" parameters used in ground motion models (e.g., magnitude and source‐to‐site distance) after conditioning on the intensity measure (IM). This paper formulates a methodology for evaluating this conditional independence assumption through the lens of causal inference (CI). From a causal perspective, the effect of an upstream parameter on the EDP of interest is partially mediated by the IM with the remainder having a direct influence on the EDP. An IM is judged to be desirable (from a conditional independence standpoint) if the mediated effect of the upstream parameter is maximized, which implies that the direct effect is minimized. In the language of causal inference (CI), the IM, EDP and upstream parameters are described as the "treatment", "outcome" and "confounding" variables, respectively. It then follows that the best performing IM is the one that maximizes the effect on the EDP after controlling for the upstream parameters. A semi‐parametric model that employs double machine learning is used to estimate the causal effect. The methodology is demonstrated through a case study application utilizing the responses from five special steel moment frames analyzed using 240 site‐agnostic ground motions. Compared to sufficiency‐based assessments, the casual inference method produces findings that are more explainable using structural dynamics principles and invariant to the number of ground motions used in the evaluation. [ABSTRACT FROM AUTHOR]

Nearest neighbor (NN) matching is widely used in observational studies for causal effects. Abadie and Imbens (2006) provided the first large‐sample analysis of NN matching. Their theory focuses on the case with the number of NNs, M fixed. We reveal something new out of their study and show that once allowing M to diverge with the sample size an intrinsic statistic in their analysis constitutes a consistent estimator of the density ratio with regard to covariates across the treated and control groups. Consequently, with a diverging M, the NN matching with Abadie and Imbens' (2011) bias correction yields a doubly robust estimator of the average treatment effect and is semiparametrically efficient if the density functions are sufficiently smooth and the outcome model is consistently estimated. It can thus be viewed as a precursor of the double machine learning estimators. [ABSTRACT FROM AUTHOR]

Effective industrial policies need to be implemented, particularly aligning with environmental protection goals to drive the high-quality growth of China’s economy in the new era. Setting up national high-tech zones falls under the purview of both regional and industrial policies. Using panel data from 163 prefecture-level cities in China from 2007 to 2019, this paper empirically analyzes the impact of national high-tech zones on the level of urban green innovation and its underlying mechanisms. It utilizes the national high-tech zones as a quasi-natural experiment and employs a double machine learning model. The study findings reveal that the policy for national high-tech zones greatly enhances urban green innovation. This conclusion remains consistent even after adjusting the measurement method, empirical samples, and controlling for other policy interferences. The findings from the heterogeneity analysis reveal that the impact of the national high-tech zone policy on green innovation exhibits significant regional heterogeneity, with a particularly significant effect in the central and western regions. Among cities, there is a notable push for green innovation levels in second-tier, third-tier, and fourth-tier cities. The moderating effect results indicate that, at the current stage of development, transportation infrastructure primarily exerts a negative moderating effect on how the national high-tech zone policy impacts the level of urban green innovation. This research provides robust empirical evidence for informing the optimization of the industrial policy of China and the establishment of a future ecological civilization system.

Abstract Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure necessary to maintain consistent sampling across years. This can result in complex and pronounced interannual changes in the observation process, which can complicate the estimation of population trends because population changes over time are confounded with changes in the observation process. Here we describe a novel modelling approach designed to estimate spatially explicit species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double machine learning, a statistical framework that uses machine learning (ML) methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. ML makes it possible to use large sets of features to control for confounding and to model spatial heterogeneity in trends. Additionally, we present a simulation method to identify and adjust for residual confounding missed by the propensity scores. To illustrate the approach, we estimated species trends using data from the citizen science project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends when faced with realistic confounding and temporal correlation. Results demonstrated the ability to distinguish between spatially constant and spatially varying trends. There were low error rates on the estimated direction of population change (increasing/decreasing) at each location and high correlations on the estimated magnitude of population change. The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions lacking rigorous monitoring data.

The long-standing, unrestrained utilization of energy resources by China’s manufacturing sector has created irreversible obstacles to regional sustainable development. Consequently, the Chinese government has implemented a water resource tax policy in certain regions, with the aim of compelling manufacturing enterprises to adopt green and energy-saving innovations. This study used panel data from Chinese manufacturing companies listed on the A-share market from 2009 to 2020 and employed a double machine learning model to explore whether the water resource fee-to-tax reform can compel enterprises to enhance their tripartite green energy-saving innovation drive. These innovations consist of vision-driven and mission-driven green energy-saving technological innovations and green management energy-saving innovations. Following a quasi-natural experiment, our findings revealed the following: (1) The water resource fee-to-tax policy promoted the internal coupling coordination of the triple-driven system. (2) The policy compelled progress in mission-driven green energy-saving technological innovations and green energy-saving management innovations but hindered vision-driven green energy-saving technological innovations. (3) Within the internal systems of manufacturing enterprises, green energy-saving management innovations play a positive mediating role between the water resource fee-to-tax policy and the mission-driven green energy-saving technology innovation subsystem, but they lack a similar positive mediating mechanism for the vision-driven green energy-saving technology innovation subsystem. (4) The counterfactual framework verified that the mechanistic pathway “water resource fee-to-tax → green energy-saving management innovation → mission-driven/vision-driven green energy-saving technological innovation” could be further extended to other manufacturing enterprises not currently under policy compulsion. (5) In the interaction system between manufacturing enterprises and external markets, the development of marketization and financial technology positively regulated the promoting effect of the water resource fee-to-tax policy on mission-driven green energy-saving technological innovations and green energy-saving management innovations, but it did not have a similar effect on vision-driven green energy-saving technological innovations.

As the world’s largest emerging market country, not only has China faced the contradiction between its huge population size and per capita energy scarcity for a long time, but the rigid constraints brought by energy poverty have also plagued the lives and production of Chinese residents. Based on panel data from 30 provinces (except Tibet) in mainland China from 2009 to 2021, this study employs double machine learning and spatial difference-in-difference for causal inference to explore the impact of a medium- to long-term regional innovation pilot policy in China—the new policy for innovative transformation in regional industrial chains—on energy poverty alleviation. This study also introduces China’s conversion of new and old kinetic energy into this quasi-natural experiment. This study presents the following findings: (1) The new policy for innovative transformation in regional industrial chains and the concept of the conversion of new and old kinetic energy can both significantly promote energy poverty alleviation. (2) The mechanism pathway of “the new policy for innovative transformation in regional industrial chains → the conversion of new and old kinetic energy → the energy poverty alleviation in heating/household electricity/transportation segments” has proved to be an effective practice in China. (3) Based on the spatial double difference model, the spatial direct effect of the new regional industrial chain innovation and change policy on energy poverty alleviation is significantly positive, while the spatial direct effect and spatial spillover effect of the new and old kinetic energy transformation on energy poverty alleviation are both significantly positive. (4) Based on the counterfactual framework analysis, in addition to the causal mediating mechanism of the demand-side conversion of new and old kinetic energy being impeded, both the supply-side and the structural-side conversion of new and old kinetic energy are able to play a significant positive causal mediating role in both the treatment and control groups.

This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China’s impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path “innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy” has significantly positive direct and indirect effects in both the treatment group and the control group. However, “innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector’s impossible triangle coupling coordination degree” and “innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector’s impossible triangle coupling coordination degree” have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group.

The article focuses on addressing the challenges related to user heterogeneity in digital experiments, specifically in detecting and analyzing heterogeneous treatment effects (HTEs). The topics covered include proposing a framework for HTE detection, and demonstrating the prevalence and significance of user characteristics in influencing experiment outcomes, emphasizing the importance of considering user heterogeneity in experimentation analysis.

Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure necessary to maintain consistent sampling across years. This can result in complex and pronounced interannual changes in the observation process, which can complicate the estimation of population trends because population changes over time are confounded with changes in the observation process.Here we describe a novel modelling approach designed to estimate spatially explicit species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double machine learning, a statistical framework that uses machine learning (ML) methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. ML makes it possible to use large sets of features to control for confounding and to model spatial heterogeneity in trends. Additionally, we present a simulation method to identify and adjust for residual confounding missed by the propensity scores.To illustrate the approach, we estimated species trends using data from the citizen science project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends when faced with realistic confounding and temporal correlation. Results demonstrated the ability to distinguish between spatially constant and spatially varying trends. There were low error rates on the estimated direction of population change (increasing/decreasing) at each location and high correlations on the estimated magnitude of population change.The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions lacking rigorous monitoring data. [ABSTRACT FROM AUTHOR]

The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data’s suppressive effect on PM2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies.

Economic growth is crucial to improve standards of living, prosperity, and welfare. R &D and knowledge spillovers can offset the diminishing returns to physical capital (machines and labor) and drive long-run growth. Market imperfections can bring R &D below the socially desired level; thus, many governments intervene to increase the stock of knowledge, and knowledge spillovers, via subsidies for R &D. We use European firm-level data to explore the effects of public subsidies on firms' R &D input and output. Average treatment effects are estimated by controlling for both observable and unobserved heterogeneity. Possible endogeneity in subsidy assignment is addressed, and the local instrumental variable (LIV) curve is identified via double machine learning methods. Results indicate that public subsidies increase both R &D intensity and output with more pronounced effects on the R &D intensity of high-technology and knowledge-intensive firms. The effects of public support remain positive and significant even after accounting for treatment endogeneity. [ABSTRACT FROM AUTHOR]

In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To deal with a broad set of macroeconomic and industry variables, we use the lasso-based double selection to estimate the predictive effects of industry distress and select relevant variables. Our sample consists of 5334 debt and loan instruments in Moody's Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. The UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires. [ABSTRACT FROM AUTHOR]

Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and regularization biases pose challenges in hybrid modeling to achieve these purposes. This paper introduces a novel approach to estimating hybrid models via a causal inference framework, specifically employing double machine learning (DML) to estimate causal effects. We showcase its use for the Earth sciences on two problems related to carbon dioxide fluxes. In the Q _10 model, we demonstrate that DML-based hybrid modeling is superior in estimating causal parameters over end-to-end deep neural network approaches, proving efficiency, robustness to bias from regularization methods, and circumventing equifinality. Our approach, applied to carbon flux partitioning, exhibits flexibility in accommodating heterogeneous causal effects. The study emphasizes the necessity of explicitly defining causal graphs and relationships, advocating for this as a general best practice. We encourage the continued exploration of causality in hybrid models for more interpretable and trustworthy results in knowledge-guided machine learning.

Geographical indications (GIs) mitigate information asymmetry in agri-food transactions by providing consumers with origin and quality information. This paper explores the impact of GIs on rural development in China by examining agricultural output and farmers’ income. Utilizing a large county-level dataset and comprehensive official GI information, this study estimates the impact of GIs on agricultural output and rural income using panel-fixed-effects models. The results reveal that GIs significantly boost agricultural added value and rural per capita disposable income. A series of methods, including difference-in-differences, propensity score matching with difference-in-differences, and double machine learning combined with difference-in-differences using random forests verify the robustness of the results. Moreover, by categorizing GIs based on product types, the analysis reveals heterogeneous effects of different GI categories on agricultural growth and income gains for farmers. The research findings in this paper offer valuable insights to inform policymaking aimed at advancing rural development, raising farmers’ incomes, and promoting sustainable agri-food supply chains.

We develop an augmented transfer regression learning (ATReL) approach that introduces an imputation model to augment the importance weighting equation to achieve double robustness for covariate shift correction. More significantly, we propose a novel seminon- parametric (SNP) construction framework for the two nuisance models. Compared with existing doubly robust approaches relying on fully parametric or fully non-parametric (machine learning) nuisance models, our proposal is more exible and balanced to address model misspecification and the curse of dimensionality, achieving a better trade-off in terms of model complexity. The SNP construction presents a new technical challenge in controlling the first-order bias caused by the nuisance estimators. To overcome this, we propose a two-step calibrated estimating approach to construct the nuisance models that ensures the effective reduction of potential bias. Under this SNP framework, our ATReL estimator is n1/2-consistent when (i) at least one nuisance model is correctly specified and (ii) the nonparametric components are rate-doubly robust. Simulation studies demonstrate that our method is more robust and efficient than existing methods under various configurations. We also examine the utility of our method through a real transfer learning example of the phenotyping algorithm for rheumatoid arthritis across different time windows. Finally, we propose ways to enhance the intrinsic efficiency of our estimator and to incorporate modern machine-learning methods in the proposed SNP framework. [ABSTRACT FROM AUTHOR]

We assess the demand effects of discounts on train tickets issued by the Swiss Federal Railways, the so-called 'supersaver tickets', based on machine learning, a subfield of artificial intelligence. Considering a survey-based sample of buyers of supersaver tickets, we use causal machine learning to assess the impact of the discount rate on rescheduling a trip, which seems relevant in the light of capacity constraints at rush hours. Assuming that (i) the discount rate is quasi-random conditional on our rich set of characteristics and (ii) the buying decision increases weakly monotonically in the discount rate, we identify the discount rate's effect among 'always buyers', who would have traveled even without a discount, based on our survey that asks about customer behavior in the absence of discounts. We find that on average, increasing the discount rate by one percentage point increases the share of rescheduled trips by 0.16 percentage points among always buyers. Investigating effect heterogeneity across observables suggests that the effects are higher for leisure travelers and during peak hours when controlling several other characteristics. • First application of causal machine learning in the public transportation literature. • Assessment of demand effects of discounts on Swiss train tickets. • 1 %-age point increase in discounts raises rescheduling by 0.16 points among customers traveling even without discount. • Effects apparently vary across several observed characteristics like reason and time of trip. [ABSTRACT FROM AUTHOR]

Evaluation of treatment effects and more general estimands is typically achieved via parametric modeling, which is unsatisfactory since model misspecification is likely. Data-adaptive model building (e.g., statistical/machine learning) is commonly employed to reduce the risk of misspecification. Naïve use of such methods, however, delivers estimators whose bias may shrink too slowly with sample size for inferential methods to perform well, including those based on the bootstrap. Bias arises because standard data-adaptive methods are tuned toward minimal prediction error as opposed to, for example, minimal MSE in the estimator. This may cause excess variability that is difficult to acknowledge, due to the complexity of such strategies. Building on results from nonparametric statistics, targeted learning and debiased machine learning overcome these problems by constructing estimators using the estimand's efficient influence function under the nonparametric model. These increasingly popular methodologies typically assume that the efficient influence function is given, or that the reader is familiar with its derivation. In this article, we focus on derivation of the efficient influence function and explain how it may be used to construct statistical/machine-learning-based estimators. We discuss the requisite conditions for these estimators to perform well and use diverse examples to convey the broad applicability of the theory. [ABSTRACT FROM AUTHOR]

China's National Ecological Civilization Demonstration Zone (NECDZ) policy has a significant role in ensuring national ecological security, and it is essential to investigate how the NECDZ policy affects the carbon emissions intensity of fisheries (CEIF) to advance China's commitment to reducing carbon emissions. This study evaluates the CEIF in 30 Chinese provinces from 2007 to 2021 using ecological civilization demonstration areas as a quasi-natural experiment and double machine learning (DML)to examine the impact and internal mechanisms of NECDZ implementation on the CEIF. We also explore spatial spillover effects using a spatial difference-in-differences approach. The results reveal that NECDZ implementation has a significantly negative impact on the CEIF and this effect continues over time. NECDZ policy potentially affects the CEIF through technology development, industrial structure improvement, and reduced energy consumption. Further investigation reveals that NECDZ implementation has spillover effects and inhibits the CEIF in surrounding regions. Therefore, it is essential to focus on developing the NECDZ policy to enhance fisheries' industrial structure, encourage low-carbon innovation in fishery technologies, and increase energy consumption efficiency. This could be supported by facilitating exchanges and cooperation with other areas, considering regional disparities, and assigning common but distinct responsibility for reducing the CEIF., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Rising energy consumption in buildings has prompted architectural engineers and policymakers to ask "what-if" questions in the pursuit of energy efficiency. Current simulation and machine learning methods rely primarily on correlations and associations necessitating causal inference techniques. In this research, a causal inferential approach combining double machine learning and domain knowledge using directed acyclic graphs is employed. The utility of this approach is demonstrated on the 2015 United States Residential energy consumption survey to evaluate the impact of energy policies and occupant behavior on cooling energy consumption. The results show that energy policies such as energy audits, proper insulation, access to interval data and Energy Star qualified windows significantly reduce energy use intensity (EUI). Additionally, air conditioning usage by occupants causally affects EUI. For instance, proper insulation reduces EUI by 5.603 kWh/m2 while changing static thermostat settings to automatic adjustments at certain times reduces EUI by 3.542 kWh/m2. This study suggests potential energy-saving policies for balanced building energy efficiency and functionality. [Display omitted] • An ML-based causal inference analysis is proposed to explain cooling energy use. • The effects of energy policy and occupant behavior on energy use are quantified. • Significant policy improvements to enhance cooling energy use are proposed. • Occupant behaviors beneficial to cooling energy use are highlighted. [ABSTRACT FROM AUTHOR]

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and low-carbon transformation of China's transportation is also an important issue in the new era. Thus, it is particularly important to correctly identify the green effect of high-speed rail. However, the traditional causal reasoning model faces several challenges such as 'dimensional curse' and multicollinearity. Based on the panel data of 283 prefecture-level cities in China from 2003 to 2019, this study uses the double machine learning model to explore the impact of transportation infrastructure upgrading on the efficiency of urban green development in China. The research shows that the upgrading of transportation infrastructure can effectively improve the efficiency of urban green development by 4%. Service industry agglomeration and green innovation are verified as two mediating channels. Moreover, the synthetic difference in difference model is employed to evaluate the regional impact of high-speed rail, and finds that the regional impact of transportation policies often exceeds the impact of individual cities. We further apply the conclusions of this paper to the research at the micro enterprise level. Goodman-Bacon decomposition and a variety of robustness tests confirm the validity of our conclusions. The study's comprehensive empirical analysis not only validates the positive effects of transportation upgrades on green development, but also offers novel insights into the underlying mechanisms and policy implications of transportation upgrading. • We investigate the nexus between transportation facilities and green development. • We employ the double machine learning model to perform quasi-merge regression. • We affirm this positive effect from the micro enterprise level. • We verify the mediation channels of service industry agglomeration and green innovation level. [ABSTRACT FROM AUTHOR]

In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To deal with a broad set of macroeconomic and industry variables, we use the lasso-based double selection to estimate the predictive effects of industry distress and select relevant variables. Our sample consists of 5334 debt and loan instruments in Moody’s Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. The UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Depression has been identified as a risk factor for suicide, yet limited evidence has elucidated the underlying pathways linking depression to subsequent suicide risk. Therefore, we aimed to examine the psychological mechanisms that connect depression to suicide risk via linguistic characteristics on Weibo. We sampled 487,251 posts from 3196 users who belong to the depression super-topic community (DSTC) on Sina Weibo as the depression group, and 357,939 posts from 5167 active users as the control group. We employed the double machine learning method (DML) to estimate the impact of depression on suicide risk, and interpreted the pathways from depression to suicide risk using SHapley Additive exPlanations (SHAP) values and tree interpreters. The results indicated an 18% higher likelihood of suicide risk in the depression group compared to people without depression. The SHAP values further revealed that Exclusive (M = 0.029) was the most critical linguistic feature. Meanwhile, the three-depth tree interpreter illustrated that the high suicide risk subgroup of the depression group (N = 1196, CATE = 0.32 ± 0.04, 95％CI [0.20, 0.43]) was predicted by higher usage of Exclusive (>0.59) and Health (>-0.10). DML revealed pathways linking depression to suicide risk. The visualized tree interpreter showed cognitive complexity and physical distress might be positively associated with suicide risk in depressed populations. These findings have invigorated further investigation to elucidate the relationship between depression and suicide risk. Understanding the underlying mechanisms serves as a basis for future research on suicide prevention and treatment for individuals with depression. • Depression contributed to a significant increase in suicide risk compared to the participants without depression. • Depression's impact on suicide risk varied across subgroups depending on linguistic features, estimated by the DML method. • Higher cognitive complexity and physical distress are related to elevated suicide risk among depressed populations. • Lower self-focus and increased leisure activities are linked with decreased suicide risk among depressed populations. [ABSTRACT FROM AUTHOR]

Certain nations have opted for stimulus-based regulations to curtail emissions, build a liveable, environmentally friendly setting, and work towards aspirational mitigation targets. New Zealand (NZ) prefers an Emission Trading Scheme (ETS) to taxation, mitigating emissions on one hand while retaining incentives for economic growth on the other. As a result, NZ has initiated a legal framework since 2008 to allow its economic sectors to engage in ETS and minimize emissions. Yet, selecting the appropriate sectors and effectively adjusting sector-specific regulations remain critical and complex challenges in the global design of ETS since both excessive and insufficient intervention can lead to inefficiencies in the system's functioning. Therefore, this study begins validating the NZ ETS's abatement potential regarding sectoral carbon intensity by executing the double machine learning techniques, consolidating the ETS efficacy that has robustly mitigated sectoral carbon intensity in NZ during 2006–2020. However, this conclusion seems invalid at the disaggregated level when focusing on forward-backward linkages, where NZ's input-output tables furnish a compelling scenario of sectoral dependencies and the products (residuals) they provide. Altogether, the regulatory requirements are either too strict or too lax, leaving out five of the 24 (as a whole) key sectors. Rather, the ETS could be powerful, providing these five key sectors are well tackled, necessitating a reformulation of the ongoing ETS regulatory regime. • NZ ETS robustly cut sectoral emission intensity at the aggregate level. • The causal effect is verified using a double machine learning model. • The NZ ETS singled out five key sectors. • NZ ETS regulatory system is invalid at the disaggregated level. • The ecological-economic Input-Output table is built to conduct analysis. [ABSTRACT FROM AUTHOR]

Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits of OPE in Markov decision processes (MDPs), where actions, rewards, and states are memoryless. We show existing OPE estimators may fail to be efficient in this setting. We develop a new estimator based on cross-fold estimation of q-functions and marginalized density ratios, which we term double reinforcement learning (DRL). We show that DRL is efficient when both components are estimated at fourth-root rates and is also doubly robust when only one component is consistent. We investigate these properties empirically and demonstrate the performance benefits due to harnessing memorylessness. [ABSTRACT FROM AUTHOR]

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