Natural disasters, deforestation, and emissions affect economic growth in Somalia.

This study ascertains the effect of natural disasters, deforestation, and emissions on economic growth in Somalia using annual time series spanning 1990-2018. Contrary to previous attempts, this study utilized the kernel regularized least squares (KRLS) technique, robust Granger causality in the presence of instabilities, and novel supremum right-tail Augment Dickey-Fuller unit root to test explosive behaviors in data series. While two date-stamped explosive behaviors are detected in economic growth (2003-2012, 2014-2016) and FDI (2004, 2016-2018), one explosive behavior is observed in capital formation (2010-2018) and population density (2010-2018). Moreover, time-varying granger causalities among sampled variables are observed. The empirical results show natural disasters and deforestation significantly undermine economic growth, whereas GHG emissions stimulate economic growth. Besides, while GHG emissions have increasing marginal effects, natural disasters and deforestation have decreasing marginal effects. The marginal effect of the interaction between natural disasters and temperature change is close to zero, implying that temperature changes do not mediate the disaster-growth nexus. Nevertheless, the study underscores the need for the implementation of environmental and economic policy reforms related to natural disaster preparedness, eliminating deforestation for charcoal exports while implementing a paradigm shift from domestic charcoal and firewood energy consumption to clean and renewable energy.

Interdependency and causality between green technology innovation and consumption-based carbon emissions in Saudi Arabia: fresh insights from quantile-on-quantile and causality-in-quantiles approaches.

In this paper, we examined the asymmetric dynamics and causality of technological progress--proxied by green technology innovation--on both consumption-based carbon (CCO2) and territory-based carbon (TCO2) emissions in Saudi Arabia using quarterly data from 1990Q1 to 2021Q4. Our initial results reject the normality and linearity assumptions of data series and thus emphasize that the observed associations are quantile dependent. We firstly utilized the quantile-on-quantile regression (QQR) approach to draw the interdependency between green technology innovation and both CCO2 and TCO2 emissions. We found a strong emission-mitigating impact of green technology innovation only at (extreme) upper emission levels. We also identified a weak positive effect at (extreme) higher emission quantiles. Furthermore, we found that higher emission levels are linked with lower green technology innovation across all emission quantiles whereas a weak positive effect is perceived at lower and medium emission quantiles. We further utilized linear and nonlinear Granger causality-in- quantiles (GCQ) tests to capture an entire picture of the impact of green technology innovation on both CCO2 and TCO2 emissions. Under linear specifications of the quantile regression model, we found evidence of strong bidirectional causality between carbon emissions and green technology innovation across lower and upper quantiles. However, we found unidirectional causalities from carbon emissions to green technology innovation at medium quantiles of the conditional distribution. Besides, there is no causality at both extreme lower and extreme upper quantiles. Under nonlinear specifications of the quantile regression model, we found a weak unidirectional causality from green technology innovation to carbon emissions at (extreme) lower quantiles. We also found a weak unidirectional causality from carbon emissions to green technology innovation at medium and extreme upper quantiles. Overall, our findings indicate that green technology innovation helps abate both CCO2 and TCO2 emissions in Saudi Arabia. Our study shows policies that target green technology innovation would significantly change carbon emissions.

Corrigendum: Association of household fuel with acute respiratory infection (ARI) under-five years children in Bangladesh.

[This corrects the article DOI: 10.3389/fpubh.2022.985445.].

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.

Mutual coupling between stock market and cryptocurrencies.

We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh.

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

Do natural disasters affect economic growth? The role of human capital, foreign direct investment, and infrastructure dynamics.

Natural disasters do occur and have become a global problem due to increasing intensity. Developing countries are mostly affected due to natural disasters owing to a poor environment, feeble adaptation, impoverished socioeconomic conditions, poor infrastructure, limited resources, and unstable institutions. The SDG 11.5 target which highlights the mitigation of loss due to natural disasters--remains crucial to achieving sustainable cities and human settlements--but the literature is limited on this scope. Thus, this research contributes to the literature by incorporating an infrastructure index, foreign direct investment (FDI), human capital index, globalization, and capital formation into the disaster-growth debate across four-income groups in 98 countries from 1995 to 2019. We developed infrastructure and human capital indices using a standard procedure across all income groups. The two-step generalized method of moments employed herein confirmed the income reduction effect of natural disasters. While the economic cost of natural disasters is relatively high in low-income countries and mild in high- and upper-middle-income countries. Besides, infrastructural development, FDI, human capital, globalization, and gross fixed capital formation also affect economic growth across income groups. Thus, the enhancement of socio-economic policies could decline economic losses, especially in vulnerable and poor settlements in developing countries.

Association of household fuel with acute respiratory infection (ARI) under-five years children in Bangladesh.

In developing countries, acute respiratory infections (ARIs) cause a significant number of deaths among children. According to Bangladesh Demographic and Health Survey (BDHS), about 25% of the deaths in children under-five years are caused by ARI in Bangladesh every year. Low-income families frequently rely on wood, coal, and animal excrement for cooking. However, it is unclear whether using alternative fuels offers a health benefit over solid fuels. To clear this doubt, we conducted a study to investigate the effects of fuel usage on ARI in children. In this study, we used the latest BDHS 2017-18 survey data collected by the Government of Bangladesh (GoB) and estimated the effects of fuel use on ARI by constructing multivariable logistic regression models. From the analysis, we found that the crude (the only type of fuel in the model) odds ratio (OR) for ARI is 1.69 [95% confidence interval (CI): 1.06-2.71]. This suggests that children in families using contaminated fuels are 69.3% more likely to experience an ARI episode than children in households using clean fuels. After adjusting for cooking fuel, type of roof material, child's age (months), and sex of the child-the effect of solid fuels is similar to the adjusted odds ratio (AOR) for ARI (OR: 1.69, 95% CI: 1.05-2.72). This implies that an ARI occurrence is 69.2% more likely when compared to the effect of clean fuel. This study found a statistically significant association between solid fuel consumption and the occurrence of ARI in children in households. The correlation between indoor air pollution and clinical parameters of ARI requires further investigation. Our findings will also help other researchers and policymakers to take comprehensive actions by considering fuel type as a risk factor as well as taking proper steps to solve this issue.

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.

Trading Kuznets curve: empirical analysis for China.

Due to inspiring growth over the past 20 years, the dynamics of Chinese exports have been the focus of many researchers. In contrast to current literature, this study examines the quadratic relationship between China's real exports to 154 partner countries and the income of trading partners from 1996 to 2019. The findings obtained from the second generational econometric analysis confirm cross-section dependence and heterogeneous slope among panel members. Second, while the GDP per capita of partner countries has a positive impact on China's exports, the quadratic of GDP per capita has a negative impact. These findings indicate an inverted U-shaped relationship between China's exports and GDP per capita of its partner countries-thus, validating the trading Kuznets curve (TKC) hypothesis. The appreciation of the Renminbi (RMB) has statistically significant negative effects on China's exports. From a policy perspective, Chinese policymakers could consider the TKC hypothesis when determining market and export strategies. Additionally, the Chinese monetary authority could consider stabilizing the value of the RMB.

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%.

Winners and losers of energy sustainability-Global assessment of the Sustainable Development Goals.

Energy sustainability plays a crucial role in achieving environmental sustainability, hence, underpins climate change mitigation. Yet, studies assessing the overarching effect of existing sustainability frameworks on energy production and consumption are limited. Here, we provide comprehensive assessment of energy sustainability across 217 countries and territories spanning 1960-2019. Using 11 targets and 15 indicators of the Sustainable Development Goals (SDGs), we present winners and losers of energy sustainability by accounting for pre-millennium development goals (MDGs), MDGs, and SDGs across income groups. While the inception of the 2030 agenda has improved energy and environmental performance across economies, low-income countries are still struggling to meet several of the SDGs. We find that sustained economic growth with reduced income inequality improves energy sustainability in developing economies. However, sustainable climate policies that reduce trade-offs between energy resources and environmental threats are highly recommended in climate-prone regions that depend heavily on water resources to boost power generation capacity.

Mitigating spread of contamination in meat supply chain management using deep learning.

Industry 4.0 recommends a paradigm shift from traditional manufacturing to automated industrial practices, especially in different parts of supply chain management. Besides, the Sustainable Development Goal (SDG) 12 underscores the urgency of ensuring a sustainable supply chain with novel technologies including Artificial Intelligence to decrease food loss, which has the potential of mitigating food waste. These new technologies can increase productivity, especially in perishable products of the supply chain by reducing expenses, increasing the accuracy of operations, accelerating processes, and decreasing the carbon footprint of food. Artificial intelligence techniques such as deep learning can be utilized in various sections of meat supply chain management--where highly perishable products like spoiled meat need to be separated from wholesome ones to prevent cross-contamination with food-borne pathogens. Therefore, to automate this process and prevent meat spoilage and/or improve meat shelf life which is crucial to consumer meat preferences and sustainable consumption, a classification model was trained by the DCNN and PSO algorithms with 100% accuracy, which discerns wholesome meat from spoiled ones.

Analyzing asymmetric effects of cryptocurrency demand on environmental sustainability.

When bitcoin (BTC), the first pioneering cryptocurrency was released in 2009, it was considered an apolitical currency. Besides, the possible effect of BTC and other cryptocurrencies on either financial markets or transactions has been widely discussed. However, the environmental effects of cryptocurrency demand have been ignored. Here, this study examines the nexus between cryptocurrencies and environmental degradation by employing standard and asymmetric causality methods. The Toda-Yamamoto and bootstrap-augmented Toda-Yamamoto test results reveal Bitcoin and Ethereum (ETH) excluding Ripple (XRP) have causal effects on environmental degradation. The Fourier-augmented Toda-Yamamoto test results show causal effects running from Bitcoin and Ripple to environmental degradation, whereas no causal effect runs from Ethereum to environmental degradation. The asymmetric causality shows causal effects from the positive shock of Bitcoin demand, negative shocks of Ripple and Ethereum demands to positive shocks of environmental degradation. Further discussions and policy implications are provided in the relevant sections of this study.

Extreme climatic effects hamper livestock production in Somalia.

Given the enormous impact of the livestock sector on Somalia's economy and its vulnerability to climate variations, this study investigates short and long-term changes in climatic effects on livestock production by using data spanning from 1985 to 2016. To this end, the ARDL bounds testing and causality techniques were employed to model the long-run and short-run relationships, and direction of causality among sampled variables. Overall, the empirical results confirmed the existence of a stable long-run cointegration between variables. Rainfall and temperature patterns were found to have a significant positive and negative impact on livestock production both in the long run and short run, respectively. The observed carbon dioxide emissions have no significant impact on livestock production in the long run but enhance livestock production in the short run. Interestingly, growth in rural population declines livestock production in the long run but not in the short run. Besides, a unidirectional causality is confirmed from temperature to rainfall and CO2 whereas livestock production has a bidirectional causal relationship with rainfall and temperature. While CO2 emissions granger cause livestock production, a unidirectional causation is observed from rural population to temperature and livestock production. This study suggests adaptation and mitigation policies that combat the negative consequences of climate change.

Asymmetric impact of energy utilization and economic development on environmental degradation in Somalia.

While there are enormous studies on climate change in stable countries, climate policy perspectives from conflict-prone regions including Somalia are limited. It is noteworthy that environmental degradation is an alarming issue that fuels the vulnerability of Somalia to climate change. To this end, this study investigates the asymmetric impact of energy and economic growth on environmental degradation in Somalia-by employing nonlinear autoregressive distributed lag model (NARDL) and causal techniques from 1985 to 2017. We find asymmetric long-term cointegration among the variables, whereas energy consumption and economic growth asymmetrically affect environmental degradation. Besides, the causal inferences reveal unidirectional causality from environmental pollution to positive change in energy consumption. Additionally, a bidirectional causality is observed between population growth and negative change in economic growth. A unidirectional causality is confirmed: from positive shock in economic growth to population growth-from a negative change in economic growth to negative shock in energy consumption-from positive change in economic growth to positive shock in energy consumption-and from a negative change in energy consumption to population growth. This calls for the implementation of clean energy investment and modern environmental strategies including good farming methods and improved grazing land policies. The adoption of these policies will improve both environmental quality and sustained economic development.

COVID-19 pandemic and economic policy uncertainty regimes affect commodity market volatility.

This paper investigates the switching effect of COVID-19 pandemic and economic policy uncertainty on commodity prices. We employ Markov regime-switching dynamic model to explore price regime dynamics of eight widely traded commodities namely oil, natural gas, corn, soybeans, silver, gold, copper, and steel. We fit two Markov switching regimes to allow parameters to respond to both low and high volatilities. The empirical evidence shows oil, natural gas, corn, soybean, silver, gold, copper, and steel returns adjust to shocks in COVID-19 outcomes and economic policy uncertainty at varying degrees--in both low volatility and high volatility regimes. In contrast, oil and natural gas do not respond to changes in COVID-19 deaths in both regimes. The findings show most commodities are responsive to historical price in terms of demand and supply in both volatility regimes. Our findings further show a high probability that commodity prices will remain in low volatility regime than in high volatility regime--owing to COVID-19-attributed market uncertainties. These findings are useful to both investors and policymakers--as precious metals and agricultural commodities show less negative response to exogenous variables. Thus, investors and portfolio managers could use precious metals, viz. Gold for short-term cover against systematic risks in the market during the period of global pandemic.

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.