Pollutant Adaptive Forecasting of India’s Greenhouse Gas and Air Pollutant Emissions Using Bayesian Dynamic Regression and Conformal Prediction
ベイズ動的回帰とコンフォーマル予測を用いたインドの温室効果ガス・大気汚染物質排出の適応的予測 (AI 翻訳)
Ravikiran Chintalapudi, Dadhu Venkata Raghunatha Reddy, Gangadhara Rao Ponugoti, Lodd Battu Bharath Raju, Sunanda A., Saritha P., Bahiru Bewket Mitikie
🤖 gxceed AI 要約
日本語
インドのCH4、CO2、N2O、NOx、PM2.5排出を1970-2024年のEDGARデータと社会経済・エネルギー・農業共変量を用いて予測する枠組みを開発。汚染物質ごとに最適なモデルを選択し、コンフォーマル予測で不確実性を評価。2025-2035年にCO2が37.96%増加と予測。
English
Develops a framework for forecasting India's CH4, CO2, N2O, NOx, and PM2.5 emissions using EDGAR data and covariates. Selects optimal model per pollutant and applies conformal prediction for uncertainty. Projects 37.96% CO2 increase from 2025 to 2035.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
インドの排出予測は日本の国際協力や炭素市場連携に関連。日本企業のインド事業における排出管理や、アジア全体の排出動向把握に示唆を与える。
In the global GX context
Provides statistical baselines for India's transition planning, relevant for global climate policy and international cooperation. Methodological insights on multi-pollutant forecasting can inform similar approaches in other countries.
👥 読者別の含意
🔬研究者:統計的予測手法と排出モデリングの応用事例として参考になる。
🏢実務担当者:インド市場での排出予測やリスク評価に活用可能。
🏛政策担当者:インドの排出削減政策立案や国際交渉の参考データを提供。
📄 Abstract（原文）
Abstract Reliable multi-pollutant emission forecasts are needed to coordinate climate mitigation and air quality management. This study developed a Pollutant Adaptive Bayesian Dynamic Regression and Conformal Prediction framework (PA-BDR-CP-X) for India’s methane (CH 4 ), carbon dioxide (CO 2 ), nitrous oxide (N 2 O), oxides of nitrogen (NO x ) and particulate matter 2.5 micrometers (PM 2.5 ) emissions using annual EDGAR emissions, version EDGAR_2025_GHG, and socioeconomic, energy and agricultural covariates for 1970–2024. Its contribution is a workflow that selects a forecasting structure separately for each pollutant from trend-only or covariate augmented Bayesian dynamic regression, autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS), and then applies conformal uncertainty calibration. Models were assessed using a fixed 2020–2024 test and 90 expanding window rolling origin forecasts per pollutant. In the fixed test, covariate augmented PA-BDR-CP-X reduced root mean square error (RMSE) relative to the best ARIMA/ETS benchmark by 35.07% for CH 4 and 18.56% for PM 2.5 . Rolling origin validation selected trend-only PA-BDR-CP-X for CH 4 and PM 2.5 , ARIMA for CO 2 and N 2 O, and ETS for NO x , demonstrating why no single model family should be imposed across pollutants. After full sample refitting, projected increases from 2025 to 2035 ranged from 12.34% for CH 4 to 37.96% for CO 2 . The 95% conformal intervals covered all 35 rolling origin evaluation forecasts per pollutant, but their width indicates conservative uncertainty bounds rather than precise or guaranteed future coverage. The forecasts provide statistical baselines for coal and energy transition planning, fertilizer and nitrogen use management, methane control in livestock, rice and waste systems, and combustion-emission controls for NO x and PM 2.5 .
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1007/s40825-026-00308-8first seen 2026-08-18 05:24:17
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