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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.

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]

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]

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]

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]

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.

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]

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]

• Study conducted a randomized controlled trial and applied Double Machine Learning. • The causality between "over-hype" and public misconceptions about AVs was identified. • Participants believed AVs are already available but not safer than human drivers. • The sociodemographic factors have little impact on the susceptibility to "over-hype." • It is necessary to regulate public communication about AVs. Traffic accidents caused by drivers' over-reliance on SAE L2 advanced driver assistance systems (ADAS) have become a new type of accident worthy of attention. There is growing concern that the "over-hype" about automation technology produced by manufacturers or the media might affect the formation of public misconceptions about autonomous vehicles (AVs), further influencing their unsafe interaction behaviors with the system. The purpose of our study was to explore whether and how "over-hype" in various contexts affects public misconceptions about AVs from the view of causality. We conducted a randomized controlled trial with a control group of 396 participants and a treatment group of 545 participants drawn from Zhenjiang, China. We applied Double Machine Learning (DML), ordered logit models, and significant difference tests to estimate the impact of "over-hype" in various contexts. The results found that the statement that "over-hype" caused public misconceptions was less rigorous. Significant causality varies among contexts of "over-hype" and aspects of misconceptions. Specifically, "over-hype" in fatal accident reports will lead to availability misconception about AVs, but not safety misconception, suggesting that participants believed AVs were available nowadays but not safe as human drivers. "Over-hype" that exaggerates ADAS capabilities has the greatest average treatment effect on public misconceptions, especially the safety misconception. We found that the young and the more educated are more likely to have safety and availability misconceptions under the "over-hype" of some contexts. However, the effects of "over-hype" varied less among groups with different socio-economic factors on the whole, which revealed the universality of the influence of "over-hype." The results provided empirical evidence for regulating propaganda about AV technology and provided practical insights on how to introduce AV technology to the general public properly. [ABSTRACT FROM AUTHOR]

In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study (Chernozhukov et al. 2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth. [ABSTRACT FROM AUTHOR]