impact quantification (social, fiscal, environmental)

This study addresses two core challenges in assessing climate change impacts on the U.S. economy: (1) quantifying uncertainties in GDP losses and nonmarket damages, and (2) reconciling divergent estimates from econometric and stated-preference models. We develop a unified climate–economy coupling framework that integrates multi-source empirical evidence, standardizes socioeconomic and climate scenarios, and jointly estimates market and nonmarket damages. This yields the first probabilistic, cross-sectorally harmonized estimate of the social cost of greenhouse gases (SC-GHG). Results indicate that the median projected U.S. GDP loss by 2100 exceeds current assessments, with a narrower but systematically biased uncertainty range—underestimating tail risks. The revised SC-GHG is substantially higher, reflecting omitted low-probability, high-impact events and international spillovers. The framework delivers a more robust, comparable, and transparent cost benchmark for climate policy.

Conventional optimization practices frequently overlook externalities and their feedback loops within socio-economic systems, leading to decisions characterized by ignorance, misjudgment, and short-termism. Method: This paper introduces an integrative framework that unifies systems thinking with externality economics—achieving, for the first time, deep coupling of normativity (value trade-offs and responsibility calibration), dynamics (feedback-loop modeling), and quantifiability (shadow pricing and social cost accounting). It employs system dynamics modeling, stakeholder mapping, and structured externality assessment to systematically identify affected parties, clarify externality transmission mechanisms, and specify *when* and *how* externalities should be incorporated into optimization processes. Contribution/Results: The framework delivers actionable pathways for embedding optimization in algorithmic governance, public policy, and AI ethics, alongside methods for responsibility calibration. It overcomes key limitations of traditional optimization—its neglect of interconnectivity, dynamic feedback, and pluralistic value structures.

Economists frequently evaluate policy effectiveness using nonlinear functions of multiple causal effect estimates—such as the Marginal Value of Public Funds (MVPF)—but often lack access to underlying microdata, relying solely on published point estimates and standard errors. When the correlation structure among these effects is unknown, conventional inference for such nonlinear functions suffers from substantial bias and poor confidence interval coverage. This paper introduces, for the first time, an asymptotically efficient and distribution-free method to construct confidence intervals for multivariate nonlinear functions without requiring microdata. Our approach integrates the delta method, robust variance propagation, sensitivity analysis, and Monte Carlo calibration. Empirically, our 95% nominal confidence intervals achieve stable coverage rates of 94.2–95.8%, markedly outperforming existing two-stage procedures. We provide an open-source software package enabling one-click inference.

This paper examines how individuals’ subjective beliefs about future income moderate the impact of tax policy on current consumption and saving decisions. Addressing the limitation of conventional policy evaluation—its neglect of expectation heterogeneity—the study establishes, for the first time, theoretical equivalence conditions between regression estimation and structural average partial effects, and proposes a three-step feasible estimator leveraging subjective belief data to jointly model belief measurement and structural policy effect identification. Methodologically, it integrates regression modeling, structural causal inference, and counterfactual prediction frameworks. Empirical analysis using Italian microsurvey data reveals that income expectations significantly attenuate or amplify the consumption response to tax changes; ignoring such beliefs leads to systematic policy effect misestimation exceeding 20%. The study thus provides a replicable methodological paradigm for expectation-driven macro-fiscal policy evaluation.

To address latent bias arising from unmeasured confounding in observational studies, this paper proposes a novel causal inference paradigm based on sample splitting: data are partitioned into planning and analysis samples, where the former adaptively selects robust design parameters (e.g., matching strategies, covariate sets), and the latter yields unbiased causal estimates. The method innovatively integrates multiple testing correction, heteroskedasticity-robust covariance estimation, and formal sensitivity analysis—extending support to multiple outcome variables, thereby relaxing the conventional single-outcome assumption. We establish theoretical guarantees of statistical validity under latent bias. Simulation studies demonstrate substantially higher statistical power than benchmark methods under strong unmeasured confounding. Empirical application to assessing the multidimensional impacts of floods in Bangladesh confirms practical feasibility and robustness.

This study addresses the challenge in applied microeconomics of effectively synthesizing empirical evidence, predicting effect sizes in new contexts, and correcting for publication bias. It proposes an integrated methodological framework that combines systematic literature review, covariate reweighting for extrapolation, and selection bias correction techniques—applicable even with as few as three prior studies. The approach innovates by offering a transparent and reproducible pipeline for out-of-sample effect prediction and, for the first time, quantifies the extent to which publication bias distorts average treatment effects. Empirical results demonstrate that bias-corrected average effects amount to only 12%–21% of naive unweighted averages, substantially improving predictive accuracy and enhancing the relevance of findings for policy design.

Geospatial impact evaluations often grapple with ambiguity in defining the exposure units, timing, and intensity of interventions, particularly when multiple plausible exposure definitions exist. This study introduces the concept of “treatment geometry” as a foundational framework to systematically characterize the spatiotemporal footprint of interventions derived from Earth observation data. Centered on key trade-offs—including spatial resolution, temporal alignment, spillover effects, and boundary uncertainty—the framework provides diagnostic tools that enable researchers to identify which geometric definition choices are most critical for causal identification, rather than defaulting to a single methodological approach. Empirical applications to air pollution, wildfires, and forest policy demonstrate that this approach substantially enhances the credibility of causal inference and the rigor of empirical design.

This study addresses the significant challenge posed by deep uncertainty in climate–economy models to climate policy evaluation. It proposes a regret-averse robust decision-making framework as an alternative to the conventional expected utility maximization paradigm. By integrating an ensemble of climate–economy models with comprehensive uncertainty analysis, the work systematically assesses the robustness of emission reduction strategies across diverse socioeconomic pathways and climate damage functions. The findings demonstrate that under conditions of high uncertainty, aggressive and rapid CO₂ mitigation measures exhibit superior robustness compared to policies derived from traditional approaches. This provides a theoretically grounded basis and actionable support for designing more resilient climate policies.

This study investigates the spillover effects of corporate toxic emissions and their network transmission mechanisms. Leveraging a panel dataset of U.S. industrial facilities from 2000 to 2023, the paper eschews pre-specified network structures and instead endogenously identifies emission impact networks through high-dimensional data, constructing a data-driven spatial panel model to flexibly estimate both direct and indirect facility-level effects. The findings reveal that approximately 28% of the total emission effect stems from indirect spillovers—substantially higher than estimates derived from conventional network specifications based on geographic proximity or industry classification—thereby exposing systematic biases in those traditional approaches. This methodology offers a more reliable network foundation for environmental risk assessment and targeted regulatory interventions.

This study addresses the uncertainty surrounding the real-world impacts of carbon offset policies on aggregate emissions and welfare, which stems in part from conventional carbon accounting metrics’ inability to capture general equilibrium spillovers. The authors develop an analytical general equilibrium model incorporating carbon offsets to systematically evaluate the effects of changes in offset prices and identify four marginal mechanisms through which offsets influence outcomes—one of which is a novel channel uncovered in this work. By integrating two dominant carbon accounting approaches into both parameterization and theoretical analysis, the study demonstrates that raising offset prices yields ambiguous effects on total emissions and welfare, suggesting that offset efficacy may be systematically over- or underestimated. These findings underscore the critical importance of incorporating general equilibrium considerations into offset policy design.