Is working less really good for the environment? A systematic review of the empirical evidence for resource use, greenhouse gas emissions and the ecological footprint

作者: Miklós Antal , Barbara Plank , Judit Mokos , Dominik Wiedenhofer

DOI: 10.1088/1748-9326/ABCEEC

关键词: Ecological crisisEconomicsEnvironmental economicsEcological footprintEmpirical evidenceSustainabilityNatural resourceWorking timeGreenhouse gasClimate change mitigation

摘要: Is reducing paid working time a potential win-win climate change mitigation strategy, which may simultaneously serve environmental sustainability and human well-being? While some researchers commentators frequently refer to such "double-dividends", most discussions ignore this topic. The societal relevance of the role its reduction as demand-side measure for mitigating climate- ecological crisis calls critical review evidence. Here we systematically empirical, quantitative literature on relationships between number indicators: resource use (incl. energy), greenhouse gas emissions footprint. We applied two comprehensive search queries in scientific databases; screened ~2,500 articles published until December 2019, used citation snowballing identify relevant research. However, only found 15 fully studies, well partially ones. This employs substantially different scopes, indicators statistical methods, each with important caveats, inhibits formal evidence synthesis but usefully informs discussion research frontier. Most studies conclude that reductions reduce pressures, primarily by decreasing incomes consumption expenditures. existing does not provide reliable guidance beyond established link expenditures impacts. Quantifying effects changes macro-economic feedbacks through productivity, employment, complementarity or substitution labour natural resources production processes has proven be difficult. To better understand impacts specific types reductions, new forms data collection at scales scopes are required. helps conceptually map pathways investigated so far crucial next steps towards more robust insights.

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