Aug 2026· International Journal of Law Management & Humanities
Abstract
The Indian legal landscape is navigating a dual transformation: the rapid digitisation of governance and an intensifying climate crisis. In the digital realm, the Digital Personal Data Protection Act, 2023 marks a fundamental shift from a consent-only regime to a framework built on Data Fiduciaries and Data Principals. As technology evolves toward decentralised systems and generative and agentic AI, however, the cyber-law framework of the Information Technology Act, 2000 faces an acute crisis of relevance, one that calls for a transition toward technological neutrality (future-proof drafting) and enhanced algorithmic clarity (AI made intelligible to humans). This paper critically evaluates the synergy between these digital frontiers and environmental law, a connection often overlooked in traditional legal discourse. It examines the ecological footprint of the digital economy, in particular the high energy consumption of data centres and the growing crisis of electronic waste, which poses a direct challenge to the constitutional guarantee of a healthy environment under Article 21. Against the backdrop of the National Education Policy 2020, the study advocates a multidisciplinary pedagogical shift that integrates green informatics into legal curricula, ensuring that future practitioners can navigate the intersection of data privacy and ecological sustainability. The paper further explores the need for legislative reforms that harmonise the requirements of the DPDP Act for data processing with environmental audits. It highlights how the integration of AI into judicial processes can streamline environmental litigation, provided such systems adhere to the principles of natural justice and preserve judicial discretion. Ultimately, the study calls for a phased, constitutionally aligned strategy that balances technological progress with constitutional morality, ensuring that India’s digital sovereignty does not come at the cost of its environmental integrity.
The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.
This R script (make_kessan10_csv.R) converts the Local Government Financial Settlement Survey (市町村別決算状況調), published by the Ministry of Internal Affairs and Communications on its annual pages of local government financial status survey materials, into machine-readable CSV. The source workbooks are print-oriented Excel files with multi-row merged headers, issued as four separate files per fiscal year (overview and expenditure, for cities and for towns and villages). The script consolidates them into long-format panels carrying fiscal year and municipality type as columns, and also writes one file per fiscal year. The output of a run over ten fiscal years (FY2015–FY2024) is deposited alongside it: all 1,741 municipalities, with 33 overview indicators and 94 expenditure items classified by purpose, giving panels of 17,410 rows each. Every municipality and every year is checked for internal consistency: the components of each expenditure category sum to that category's total, and the sum of all categories matches the total expenditure reported in the overview table. All checks passed for all ten years. Amounts are in thousands of yen, as published; blank cells are left blank rather than filled with zero. The column structure of the source data does not change over the period covered. One definitional change affects the adjusted ratio of current expenditure to current revenue: for FY2020 and FY2021 the special bonds issued for deferred tax collection are removed from current general revenue as well. Four changes of municipality occurred: Tomiya and Nakagawa became cities in FY2016 and FY2018 respectively, each receiving a new municipality code; Sasayama was renamed Tamba-Sasayama in FY2019, and Aogashima was renamed in FY2018 in the written form of its name only, both keeping their codes. The code was written with generative AI: Claude (Anthropic) was used to write and revise it. The author has verified the output and takes responsibility for the content. Version 1.1 corrects the reading of the census population change column in the overview table, where a small negative rate written with the triangle sign used in Japanese official statistics was left blank instead of being read as a number. 56 cells across the ten years were affected; no other value changed.
Yasutoshi Moteki· Zenodo (CERN European Organi...· 1 citation
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.