Assessment of global fish footprint reveals growing challenges for sustainable production and consumption

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Bibliometric analysis of water-energy-food nexus : Sustainability assessment of renewable energy

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Global land-use intensity and anthropogenic emissions exhibit symbiotic and explosive behavior

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Dataset on bitcoin carbon footprint and energy consumption

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Global estimation of mortality, disability-adjusted life years and welfare cost from exposure to ambient air pollution

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Global assessment of environment, health and economic impact of the novel coronavirus (COVID-19)

The institution of social distancing and punitive measures to contain the spread of COVID-19 through human-to-human transmission has environmental, health and economic impact. While the global pandemic has led to the enhancement of the health system and decline of emissions, economic development appears deteriorating. Here, we present the global environmental, health and economic dimension of the effect of COVID-19 using qualitative and empirical assessments. We report the health system policies, environmental sustainability issues, and fiscal, monetary and exchange rate measures introduced during lockdown across countries. While air pollution is reported to have declined, municipal and medical waste is increasing. The COVID-19 global pandemic uncertainty ranks the UK as the country with the highest uncertainty level among 143 countries. The USA has introduced 100% of pre-COVID-19 crisis level GDP, the highest policy cut-rate among 162 countries. Science, innovation, research and development underpin COVID-19 containment measures implemented across countries. Our study demonstrates the need for future research to focus on environment-health-economic nexus—a trilemma that has a potential trade-off.publishedVersionUnit Licence Agreemen

How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls)

The application of dynamic Autoregressive Distributed Lag (dynardl) simulations and Kernel-based Regularized Least Squares (krls) to time series data is gradually gaining recognition in energy, environmental and health economics. The Kernel-based Regularized Least Squares technique is a simplified machine learning-based algorithm with strength in its interpretation and accounting for heterogeneity, additivity and nonlinear effects. The novel dynamic ARDL Simulations algorithm is useful for testing cointegration, long and short-run equilibrium relationships in both levels and differences. Advantageously, the novel dynamic ARDL Simulations has visualization interface to examine the possible counterfactual change in the desired variable based on the notion of ceteris paribus. Thus, the novel dynamic ARDL Simulations and Kernel-based Regularized Least Squares techniques are useful and improved time series techniques for policy formulation. • We customize ARDL and dynamic simulated ARDL by adding plot estimates with confidence intervals. • A step-by-step procedure of applying ARDL, dynamic ARDL Simulations and Kernel-based Regularized Least Squares is provided. • All techniques are applied to examine the economic effect of denuclearization in Switzerland by 2034.publishedVersionUnit Licence Agreemen

Escalation effect of fossil-based CO2 emissions improves green energy innovation

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Impact of meteorological factors on COVID-19 pandemic : Evidence from top 20 countries with confirmed cases

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Global effect of city-to-city air pollution, health conditions, climatic & socio-economic factors on COVID-19 pandemic

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