The Application of Green GDP and Its Impact on Global Economy and Environment: Analysis of GGDP based on SEEA model

Mingpu Ma

Home/Archive/arXiv

arXiv · 2409.02642

The Application of Green GDP and Its Impact on Global Economy and Environment: Analysis of GGDP based on SEEA model

Mingpu Ma

View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper presents an analysis of Green Gross Domestic Product (GGDP) using the System of Environmental-Economic Accounting (SEEA) model to evaluate its impact on global climate mitigation and economic health. GGDP is proposed as a superior measure to tradi-tional GDP by incorporating natural resource consumption, environmental pollution control, and degradation factors. The study develops a GGDP model and employs grey correlation analysis and grey prediction models to assess its relationship with these factors. Key findings demonstrate that replacing GDP with GGDP can positively influence climate change, partic-ularly in reducing CO2 emissions and stabilizing global temperatures. The analysis further explores the implications of GGDP adoption across developed and developing countries, with specific predictions for China and the United States. The results indicate a potential increase in economic levels for developing countries, while developed nations may experi-ence a decrease. Additionally, the shift to GGDP is shown to significantly reduce natural re-source depletion and population growth rates in the United States, suggesting broader envi-ronmental and economic benefits. This paper highlights the universal applicability of the GGDP model and its potential to enhance environmental and economic policies globally.

Explore related subjects

econ.EM

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Mingpu Ma. 2024-09-04. The Application of Green GDP and Its Impact on Global Economy and Environment: Analysis of GGDP based on SEEA model. https://arxiv.org/abs/2409.02642

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Difference-in-Differences with Unpoolable Data

Difference-in-differences (DID) is commonly used to estimate treatment effects but is infeasible in settings where data are unpoolable due to privacy concerns or legal restrictions on data sharing, particularly across jurisdictions. In this study, we identify and relax the assumption of data poolability in DID estimation. We propose an innovative approach to estimate DID with unpoolable data (UN-DID) which can accommodate covariates, multiple groups, and staggered adoption. Through analytical proofs and Monte Carlo simulations, we show that UN-DID and conventional DID estimates of the average treatment effect and standard errors are equal and unbiased in settings without covariates. With covariates, both methods produce estimates that are unbiased, equivalent, and converge to the true value. The estimates differ slightly but the statistical inference and substantive conclusions remain the same. Two empirical examples with real-world data further underscore UN-DID's utility. The UN-DID method allows the estimation of cross-jurisdictional treatment effects with unpoolable data, enabling better counterfactuals to be used and new research questions to be answered.

econ.EM↗

arXiv2026

The Promise of Time-Series Foundation Models for Agricultural Forecasting: Evidence from Commodity Prices

Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds with modern time-series foundation models (TSFMs). Using USDA ERS monthly commodity price data from 1997-2025, we evaluate 17 forecasting approaches across four model classes, including traditional time-series, machine learning, deep learning, and five state-of-the-art TSFMs (Chronos, Chronos-2, TimesFM 2.5, Time-MoE, Moirai-2), and construct annual marketing year price predictions to compare with USDA's futures-based season-average price (SAP) forecasts. We show that zero-shot foundation models consistently outperform traditional time-series methods, machine learning, and deep learning architectures trained from scratch in both monthly and annual forecasting. Furthermore, foundation models remarkably outperform USDA's futures-based forecasts on three of four major commodities despite USDA's information advantage from forward-looking futures markets. Time-MoE delivers the largest accuracy gains, achieving 54.9% improvement on wheat and 18.5% improvement on corn relative to USDA ERS benchmarks on recent data (2017-2024 excluding COVID). These results point to a paradigm shift in agricultural forecasting.

econ.EM↗

arXiv2026

Testing for Monotone Equilibrium Strategies in Games of Incomplete Information

This paper develops a unified framework for testing monotonicity of Bayesian Nash equilibrium strategies in unobserved types in games of incomplete information. We show that, under symmetric independent private types, monotonicity of differentiable equilibrium strategies is equivalent to monotonicity of a quasi-inverse strategy identified from observed actions. This allows the problem to be reformulated as testing a countable set of moment inequalities involving unconditional expectations. We propose a Cramer-von Mises-type statistic with bootstrap critical values. The method accommodates covariates and game heterogeneity. Monte Carlo simulations demonstrate finite-sample performance, and an application to procurement auctions illustrates cartel detection.

econ.EM↗