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

Globalization faces a tradeoff between meeting fish consumption demand for nutritious & healthy living and reducing the ecological footprint to achieve sustainable development. Here, we document drivers, historical trends, and mitigation options for global fish footprint using unevenly spaced data spanning 1961 to 2021 from over 200 economies while accounting for income classifications. We report a decline in fish production in developed countries, yet, their increased consumption demand per capita is met through overexploited stocks of fish imported from developing economies. Besides, global fish price volatility has no effect on fish distribution in high-income nations but highly influences fish production, consumption, import, and export in developing nations. The evidence of purchasing economies of scale in urbanized countries and the potential threat of embodied price in fish distribution and trade affect global fish footprint. The persistent increase in fish footprint can be attributed to affluence, choice of technology, urbanization, human development, marine trophic levels, emission intensity, and time-invariant & unobserved country-specificities. We highlight that aligning development and choices along the targets of sustainable development goals augments the achievement of sustainable fish production and consumption.

Comprehensive green growth indicators across countries and territories.

A sustainable transition to green growth is crucial for climate change adaptation and mitigation. However, the lack of clear and consistent definitions and common measures for green growth implies a disagreement on its determinants which hampers the ability to proffer valuable guidance to policymakers. We contribute to the global debate on green economic development by constructing green growth measures from 1990 to 2021 across 203 countries. The pillars of green growth are anchored on five dimensions namely natural resource base, socio-economic outcomes, environmental productivity, environmental-related policy responses, and quality of life. Contrary to the aggregated methods used in constructing indices in the extant literature, we employ a novel summary index technique with generalized least squares attributed-standardized-weighted index that controls for highly correlated variables and missing values. The constructed indicators can be used for both country-specific and global data modeling on green economic development useful for policy formulation.

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

The intensification of land use is accelerating and remains a threat to achieving environmental sustainability. Although prior literature identifies unsustainable demand for resources as crucial to ecosystem vitality, we highlight explosive behavior and indicators associated with changing global land-use intensity and emissions. We assess emission footprints, forestry, and agricultural land-use intensity across income groups. We find that long-term income growth above US$1005/capita has mitigation effects on emissions, whereas emissions stimulate the global expansion of land use for agricultural and forestry activities. Urban expansion has diminishing effects on agricultural lands in developed countries, which may alter future agricultural production and food consumption. The heterogeneous effects across countries demonstrate the need for domestic context, including cultural and historical factors, in assessing forest decline, agricultural expansion, and land-use intensity. The co-benefits of Reducing Emissions from Deforestation and Forest Degradation (REDD+) in developing economies are crucial to mitigating emissions while improving forest-dependent livelihoods.

Dataset on bitcoin carbon footprint and energy consumption.

Due to data limitations on bitcoin-related emissions, assessing the environmental impacts of bitcoin appear difficult. This data in brief article presents constructed daily frequency dataset on bitcoin annualised carbon footprint spanning July 7, 2010 to December 4, 2021 with 4,158 observations. The 12 data variables capture floor, ceiling, and optimal annualised carbon footprint from coal, oil, gas, and the average from the 3 sources. The constructed bitcoin carbon footprint data are measured in kgCO2 using emission factors for electricity generation from IEA World Energy Outlook. The data will benefit multidisciplinary research on cryptocurrency from environmental, energy, and economics disciplines.

COVID-19 pandemic improves market signals of cryptocurrencies-evidence from Bitcoin, Bitcoin Cash, Ethereum, and Litecoin.

The COVID-19 global pandemic has disrupted business-as-usual, hence, affecting sustained economic development across countries. However, it appears economic uncertainty following COVID-19 containment measures favor market signals of cryptocurrencies. Here, this study empirically and structurally investigates the implication of COVID-19 health outcomes on market prices of Bitcoin, Bitcoin Cash, Ethereum, and Litecoin. Evidence from the novel Romano-Wolf multiple hypotheses reveal COVID-19 shocks spur Litecoin by 3.20-3.84%, Bitcoin by 2.71-3.27%, Ethereum by 1.43-1.75%, and Bitcoin Cash by 1.34-1.62%.

Ambient air pollution and meteorological factors escalate electricity consumption.

The impact of climate change is evident in the variability of weather patterns, hence, affecting electricity generation and consumption. Existing literature examines the effect of humidity and temperature on energy, but suffers from omitted variable bias. Here, we adopt several parameters namely ambient air pollution, precipitation, surface pressure, dew-frost point, relative humidity, wind speed, earth skin temperature, cooling degree days, heating degree days, solar and wind generation, cumulative installed PV power, and wind turbine capacity, solar and wind electricity consumption, and energy price index to investigate the role of climatic and energy-related factors on households, industry sector, commercial and public service attributed electricity consumption in Norway. Our machine learning estimator accounts for climate change heterogeneity, and historical effects while controlling omitted-variable and misspecification bias. The empirical assessment shows the radiative forcing effect of ambient air pollution decreases electricity consumption. In contrast, the scavenging effect of rainfall intensity on ambient air pollution improves both wind and solar electricity consumption. Rising levels of earth skin temperature, and humidity increases solar and wind electricity consumption whereas dew-frost point drops temperature, and humidity to improve human comfort. Our study highlights that energy price index is critical to the adoption of solar and wind energy technologies.

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

The rate of spread of the global pandemic calls for much attention from the empirical literature. The limitation of extant literature in assessing a comprehensive COVID-19 portfolio that accounts for complexities in the spread and containment of the virus underscores this study. We investigate the effect of city-to-city air pollutant species, meteorological conditions, underlying health conditions, socio-economic and demographic factors on COVID-19 health outcomes. We utilize a panel estimation of 615 cities in 6 continents from January 1 to June 11, 2020. While social distancing measures, movement restrictions and lockdown are reported to have improved environmental quality, we show that ambient PM2.5 remains unhealthy and above the acceptable threshold in several countries. Our empirical assessment shows that while ambient PM2.5, nitrogen dioxide, ozone, pressure, dew, Windgust, and windspeed increase the spread of COVID-19, high relative humidity and ambient temperature have mitigation effect on COVID-19, hence, decreases the number of confirmed cases. We report 66.3% of countries projected to experience a second wave of COVID-19 if government stringency and safety protocols are not enhanced. By extension, our assessments demonstrate that several factors namely underlying health conditions, meteorological, air pollution, health system quality, socio-economic and demographics spur the reproduction effect of COVID-19 across countries. Our study highlights the importance of government stringency in containing the spread of COVID-19 and its impacts.

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

The 21st-century development pathway is facing a challenge between climate change mitigation, sustained economic prosperity, and energy security. While extant literature focuses on drivers of anthropogenic emissions, the role of policy measures including green energy innovation, and energy research and development are limited in scope. Here we develop conceptual tools across IEA member countries with four decades of data that demonstrate the role of green energy innovation, and research and development in reducing emissions. Our assessment reveals that sectoral fossil-based CO2 contributes directly to GHG emissions by 29.7-40.6% from transport, 24.6-32% from industry, 18.6-19.5% from buildings, 15-18.4% from other sectors, and 0.5-1.1% from power. We highlight that industrialized high-income countries converge on green energy innovation but diverge on emissions. The empirical evidence shows that achieving green growth is possible through green energy innovation amidst climate change and its impact.

Relationship between mortality and health care expenditure: Sustainable assessment of health care system.

Infant and maternal mortality are important indicators for assessing the quality of healthcare systems. The World Health Organization underscores the importance of proper health care system in reducing preventable mortality through early intervention. Early intervention includes availability, accessibility and affordability of health care systems for children and mothers. While there are several studies that assess the immediate and underlying drivers of child mortality, literature on the role of policy measures are limited and inconsistent. Thus, robust empirical analysis of the determinants of maternal and infant mortality remains inconclusive in the era of achieving the Sustainable Development Goals (SDG). Here, we examined the influence of health expenditure on infant and maternal deaths for the period 2000-2015 across 177 countries. Using panel Quantile Regression with bootstrapping, this study accounted for the 2007-2008 financial crisis in an empirical relationship between health outcome and health expenditure. We found a negative effect of health expenditure on mortality across all percentiles. Infant mortality rate declines between 0.19% - 1.45% while maternal mortality rate declines ranging from 0.09% - 1.91%. To attain the goal of ensuring healthy lives and wellbeing of all people (SDG 3), this study infers that health expenditure potentially reduces maternal and infant mortality across lower and middle income countries. We highlight the need for an enhanced health care expenditure, especially in developing countries to curb the levels of infant and maternal deaths.

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.

Impact of COVID-19 pandemic on waste management.

The containment of the spread of COVID-19 pandemic and limitations on commercial activities, mobility and manufacturing sector have significantly affected waste management. Waste management is critical to human development and health outcomes, especially during the COVID-19 pandemic. The invaluable service provided by the waste management sector ensures that the unusual heaps of waste that poses health risks and escalate the spread of COVID-19 is avoided. In this study, we assess the impact of COVID-19 pandemic on waste management by observing lockdown and social distancing measures. We found that the quantity of waste increased across countries observing the social distancing measure of staying at home. The intensification of single-use products and panic buying have increased production and consumption, hence thwarting efforts towards reducing plastic pollution. However, several countries have thus far instituted policies to ensure sustainable management of waste while protecting the safety of waste handlers.

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.

How to apply dynamic panel bootstrap-corrected fixed-effects (xtbcfe) and heterogeneous dynamics (panelhetero).

The characteristics of panel data namely, inter alia, missing values, cross-sectional dependence, serial correlation, small time period bias, omitted variable bias, country-specific fixed-effects, time effects, heterogeneous effects and convergence often lead to misspecification, and spurious regression, thus, affecting the consistency and robustness of the model. In this regard, a more sophisticated panel estimation technique that accounts for the attributes and challenges is worthwhile. The novel panel bootstrap-corrected fixed-effects estimator (xtbcfe) and heterogeneous dynamics (panelhetero) recommended in this study meets almost all the requirements for robust and consistent panel estimation with an interface for user modifications. We further demonstrate how to use empirical CDF, moments and kernel density estimation to investigate heterogeneous effects. Due to the complexities in the application of xtbcfe and panelhetero algorithm, we provide a step-by-step procedure and guidelines for the estimation approach. We apply the xtbcfe and panelhetero algorithm for global estimation of mortality, disability-adjusted life years and welfare cost from exposure to ambient air pollution. Importantly, the xtbcfe algorithm can be applied to any panel data-based studies in social science, environmental science, environmental economics, health economics, energy economics, and among others.•Procedures useful for data imputation and transforming negative variables for time series, cross-sectional and panel data are presented.•Contrary to traditional models, we show how a novel approach can be modified and used to examine the degree of heterogeneous effects across cross-sectional units of panel data.•We demonstrate how the dynamic panel bootstrap-corrected fixed-effects estimator is useful in estimating higher-order panel data models and accounting for challenges such as omitted-variable bias, convergence, cross-section dependence and heterogeneous effects.•We apply the imputation technique, panelhetero, and xtbcfe algorithms to examine the nexus between ambient air pollution and health outcomes.

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

The global confirmed cases of COVID-19 have surpassed 7 million with over 400,000 deaths reported. However, 20 out of 187 countries and territories have over 2 million confirmed cases alone, a situation which calls for a critical assessment. The social distancing and preventive measures instituted across countries have a link with spread containment whereas spread containment is associated with meteorological factors. Here, we examine the effect of meteorological factors on COVID-19 health outcomes. We develop conceptual tools with dew/frost point, temperature, disaggregate temperature, wind speed, relative humidity, precipitation and surface pressure against confirmed cases, deaths and recovery cases. Using novel panel estimation techniques, our results find strong evidence of causation between meteorological factors and COVID-19 outcomes. We report that high temperature and high relative humidity reduce the viability, stability, survival and transmission of COVID-19 whereas low temperature, wind speed, dew/frost point, precipitation and surface pressure prolong the activation and infectivity of the virus. Our study demonstrates the importance of applying social distancing and preventive measures to mitigate the global pandemic.

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

Environmental pollution in the era of sustained economic development is an inevitable occurrence. However, the rising levels of pollutant emissions hamper air quality, hence, affecting health outcomes. Previous studies have assessed the case-by-case effect of ambient air pollution on mortality and morbidity, however, the impact on disability-adjusted life years (DALYs) and welfare cost have not been investigated entirely. Here, we conduct an empirical analysis of the 28-Year trend to analyze the nexus between ambient particulate matter and ozone, mortality, DALYs, and welfare cost across 195 countries and territories by employing novel dynamic panel estimation methods. We find that none of the 195 countries and territories studied between 1990 and 2017 meet WHO guideline for air quality, thus, mitigating ambient air pollution is at risk. However, Spain with an annual average of PM2.5 not exceeding 15.12 µg/m3 is closer to WHO guideline of 10 µg/m3/annum. Among the countries (China, the US, Russia, India, Germany and Japan) with the highest welfare cost of premature death associated with the exposure to outdoor PM2.5 and ozone, China is the most vulnerable to economic burden - spending US$1.58 trillion (constant 2010) in 2017. This study demonstrates that ambient air pollution has a significant impact on economic development (welfare cost) and health outcomes (mortality, premature deaths, and DALYs).

Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques.

The initial investigation by local hospital attributed the outbreak of the novel coronavirus disease (COVID-19) to pneumonia with unknown cause that appeared like the 2003 severe acute respiratory syndrome (SARS). The World Health Organization declared COVID-19 as public health emergency after it spread outside China to several countries. Thus, an assessment of the novel coronavirus disease (COVID-19) with novel estimation approaches is essential to the global debate. This study is the first to develop both time series and panel data models to construct conceptual tools that examine the nexus between death from COVID-19 and confirmed cases. We collected daily data on four health indicators namely deaths, confirmed cases, suspected cases, and recovered cases across 31 Provinces/States in China. Due to the complexities of the COVID-19, we investigated the unobserved factors including environmental exposures accounting for the spread of the disease through human-to-human transmission. We used estimation methods capable of controlling for cross-sectional dependence, endogeneity, and unobserved heterogeneity. We predicted the impulse-response between confirmed cases of COVID-19 and COVID-19-attributable deaths. Our study revealed that the effect of confirmed cases on the novel coronavirus attributable deaths is heterogeneous across Provinces/States in China. We found a linear relationship between COVID-19 attributable deaths and confirmed cases whereas a nonlinear relationship was confirmed for the nexus between recovery cases and confirmed cases. The empirical evidence revealed that an increase in confirmed cases by 1% increases coronavirus attributable deaths by ~0.10%-~1.71% (95% CI). Our empirical results confirmed the presence of unobserved heterogeneity and common factors that facilitates the novel coronavirus attributable deaths caused by increased levels of confirmed cases. Yet, the role of such a medium that facilitates the transmission of COVID-19 remains unclear. We highlight safety precaution and preventive measures to circumvent the human-to-human transmission.

Mitigating degradation and emissions in China: The role of environmental sustainability, human capital and renewable energy.

China's carbon-embedded growth trajectory is gradually becoming a burden to environmental sustainability, hence, requires much attention. The complexity of human capital attributed emissions coupled with fossil fuel inclined energy utilization for industrialization underscores the failure of China to meet its mitigation target. We developed a policy-driven conceptual tool based on disaggregate energy utilization, human capital, trade, income level and natural resource exploitation in a carbon and environmental degradation function. Using a battery of statistics and econometric techniques such as neural network, SIMPLS, U test, dynamic ARDL Simulations, and Prais-Winsten first-order autoregressive [AR(1)] regression with robust standard errors, we examined the theme based on a data spanning 1961-2016. The study demonstrates that fossil fuel energy consumption and human capital are conducive catalysts for climate change. The instantaneous increase in renewable energy, environmental sustainability and income level has a diminishing effect on emissions and environmental degradation. The environmental Kuznets curve (EKC) hypothesis is validated in both emissions and degradation function - at a turning point of US$ 5469.79 and US$ 5863.70, respectively. The study highlights that the over-dependence on fossil fuel energy and natural resources for economic development, carbon-intensive trade and carbon-embedded human capital, thwart efforts to mitigating climate change and its impacts. Thus, the onus of responsibility for achieving a cleaner environment in China depends majorly on governmental policies that favour or dampens environmental sustainability.

Determinants of renewable energy consumption in Africa.

Although the role that renewable energy consumption plays on economic growth and emissions has been widely studied, there are relatively few papers focusing on the determinants of renewable energy consumption, and only one study focuses on the factors related to the share of renewables in the energy consumption in Africa. This paper contributes to the literature by filling the gap in knowledge by exploring the nexus between the share of renewables in energy consumption and social and economic variables, for a panel consisting of 21 African countries for the period between 1990 and 2013, extending the set of variables and the time span used by a previous study. Estimating a random-effects generalized least squares regression, we find that countries with a higher Human Development Index and a higher gross domestic product per capita have a lower share of renewable energy in the national grid. On the other hand, an increase in foreign direct investment has been found to be related to higher renewable energy integration. The level of democracy, measured by the Freedom House political rights and civil liberties ratings, does not directly affect the integration level of renewable energy sources. The negative relationship between gross domestic product per capita and the share of renewables contradicts previous findings for developed countries. This contradiction and policy implications are discussed in the light of the review of the energy mix of the selected countries.

Environmental sustainability assessment using dynamic Autoregressive-Distributed Lag simulations-Nexus between greenhouse gas emissions, biomass energy, food and economic growth.

Increasing population demand has triggered the enhancement of food production, energy consumption and economic development, however, its impact on climate change has become a global concern. This study applied a novel environmental sustainability assessment tool using dynamic Autoregressive-Distributed Lag (ARDL) simulations for model estimation of the relationships between greenhouse gas (GHG) emissions, energy, biomass, food and economic growth for Australia using data spanning from 1970 to 2017. The study found an inversed-U shaped relationship between energy consumption and income level, showing a decarbonized and services economy, hence, improved energy efficiency. While energy consumption increases emissions by 0.4 to 2.8%, biomass consumption supports Australia's transition to a decarbonized economy by reducing GHG emissions by 0.13% and shifts the demand for fossil fuel. Food and energy consumption underpin socio-economic development and vice versa. However, food waste from production and consumption increases ecological footprint, implying a lost opportunity to improve food security and reduce environmental pressure from agricultural production. There is no single path to achieving environmental sustainability, nonetheless, the integrated approach applied in this study reveals conceptual tools which are applicable for decision making.

Does energy consumption follow asymmetric behavior? An assessment of Ghana's energy sector dynamics.

The study answered the following questions: First, does energy evolves in different regimes by transitioning over a finite set of unobserved states? Second, does energy consumption follow an asymmetric behavior over "energy boom" and energy scarcity? and, Third, are there unobserved factors underpinning energy crisis? We employed Markov-switching dynamic regression to examine the asymmetric effect, NIPALS regression to examine energy determinants and neural network analysis for prediction. The neural network model suggests a 99% prediction of energy consumption by the predictor variables. It was evident that energy consumption evolves in two states by transitioning over a finite set of unobserved states. The 11.6% growth in energy consumption is expected to occur in 4.1 years while energy crisis is expected to last for 3.7 years. Technological advancement and the development of green energy through foreign direct investment are essential to improve energy sector portfolio.