Can data science achieve the ideal of evidence-based decision-making in environmental regulation?

Kim, Eun-Sung

doi:10.1016/j.techsoc.2024.102615

Cited by 3 publications

References 30 publications

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Enhancing environmental decision-making: a systematic review of data analytics applications in monitoring and management

Ncube,

Ngulube

2024

Discov Sustain

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Escalating environmental challenges necessitate paramount decision-making to safeguard ecosystems and resources. However, the burgeoning volume and intricate nature of environmental data often present a formidable challenge in gleaning actionable insights. In this context, integrating data analytics tools within environmental monitoring and management frameworks offers a compelling avenue for progress. These tools facilitate efficient data processing, uncover hidden patterns, and enable predictive modelling, leading to more informed decisions. Despite growing research, a comprehensive understanding of specific data analytics applications, methodologies, and demonstrably effective implementations remains elusive. This systematic review aimed to address this gap. Following PRISMA guidelines, a meticulous search across five databases was conducted using predefined inclusion/exclusion criteria. Rigorous data extraction captured salient study characteristics, methodologies, data analysis techniques, key findings, and acknowledged limitations. The review revealed that data analytics offers a powerful toolkit for environmental management, transforming decision-making across all stages. Big data and advanced techniques enable proactive strategies through earlier issue detection and improved predictive models. However, maximising this potential requires a multifaceted approach, including standardised data collection, data literacy, ethical frameworks, and stakeholder engagement.

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Enhancing environmental decision-making: a systematic review of data analytics applications in monitoring and management

Ncube,

Ngulube

2024

Discov Sustain

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Forest owners’ perceptions of machine learning: Insights from swedish forestry

Wising,

Sandström,

Lidberg

2024

Environmental Science & Policy

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What's in a Name?

Kobiyh,

Ed-Dafali

2024

Advances in Logistics, Operations, and Management Science

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The development of financial mechanisms and markets in recent years has been driven by efforts to reduce emissions and protect environmental quality. Key stakeholders, including lenders, environmentally focused project promoters, and investors using non-financial criteria, have played a role in this shift. Environmental finance has emerged from this evolution, shaped by innovations and regulations, and now holds a central place in modern finance. This chapter examines how environmental finance reintegrates into the financial landscape, fostering market growth and new financial products. It highlights how policies and regulations can drive clean industry innovation, enhance investor awareness, and guide decision-makers toward climate-friendly investments.

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Can data science achieve the ideal of evidence-based decision-making in environmental regulation?

Cited by 3 publications

References 30 publications

Enhancing environmental decision-making: a systematic review of data analytics applications in monitoring and management

Enhancing environmental decision-making: a systematic review of data analytics applications in monitoring and management

Forest owners’ perceptions of machine learning: Insights from swedish forestry

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