The impact of agriculture production and renewable energy consumption on CO2 emissions in developing countries: the role of governance.

Environmental pollution has aggravated the climate change issues posing unusual challenges to the survival and growth of humanity, including extreme weather, loss of species, and sustainability of the ecosystem in developing countries. Unlike previous studies, this paper adds new dimension to the literature by incorporating corruption into agriculture production-environment nexus. This study adds new dimension to the literature by examining corruption, agriculture, and renewable energy on CO2 emissions. The study therefore examines the effects of governance (corruption) and agriculture production on CO2 emissions in 20 countries in Africa from 1990 to 2019. The study employed recent panel econometric approach which accounts for cross-sectional dependence in the variables. The findings of the fixed effect model and panel dynamic ordinary least squares (PDOLS) show that forest and renewable energy consumption decrease CO2 emissions. However, corruption, agriculture production, export, and urbanization escalate CO2 emissions in African countries covered in the paper. Moreover, the Dumitrescu-Hurlin Granger causality indicates a bidirectional causality between agriculture production and CO2 emissions, renewable energy use, agricultural output, and forest. Also, unidirectional Granger causality runs from corruption to forest and agriculture production. On these premises, consented efforts by governments should be made to support good institutions in order to promote good governance to avert pervasive consequences of corruption on the environment.

Predicting the carbon dioxide emission of China using a novel augmented hypo-variance brain storm optimisation and the impulse response function.

For the past decade, the level of carbon dioxide emission in most cities in China is on the ascendancy. Yet, better prediction of environmental pollution is at the fringes of recent studies. Several erstwhile researchers have attempted predicting pollution whilst utilising approaches including the ordinary linear regressions, multivariate regressions, autoregressive integrated moving average (ARIMA), evolutionary and some conventional swarm intelligence. These conventional approaches, however, lead but to imprecise predictions owing to the inherent parameter problems characterised in those approaches. Consequently, there is the need for a better prediction of the key antecedents that affect air pollution whilst using robust techniques. This current study, therefore predicts the carbon emissions levels of China into the next decade, in response to changes in key economic variables: energy consumption, economic growth, trade, and urbanisation. This is to aid in monitoring and implementing of tailored policies and transformations in China and in similar developing and emerging economies. Our findings revealed a steadily rise in emissions as the economy grows during the initial years but decline in the ensuing forecasted period. The findings of the impulse response function, revealed that in the next decade, urbanisation, and trade (import and export) will be the major contributors of carbon dioxide emission. The proposed Brainstorm optimisation algorithms prediction model was verified and validated with actual data. Our study revealed that the Brainstorm Optimisation algorithm predicts better with less prediction error even under uncertainty information.

The influence of trade openness on environmental pollution in EU-18 countries.

Trade openness is one of the main channels of globalization and technological transfers. In environmental economic literature, the implications of trade openness remain controversial and still could be potential drivers of carbon dioxide emissions. This study therefore explores the effect of trade openness in developed countries using EU-18 economies. We employed an econometric approach that accounts for cross-section dependence among study variables. The panel CIPS and CADF unit root show that the variables are stationary and the long-run relationship was confirmed in Westerlund cointegration tests. The mean group (MG) and augmented mean group (AMG) results show that trade openness increases CO2-emissions in EU-18. Again, energy consumption and urbanization escalate emissions. The study confirmed the environmental Kuznets curve. Finally, pollution halo and pollution haven hypothesis were confirmed at both estimation methods. The Dumetriscu-Hurlin Granger causality test results confirmed bidirectional causality between trade openness and energy consumption and between trade openness and economic growth. Again, unidirectional Granger causality is running from trade openness and CO2 emissions. Policy recommendations are further proposed.

The Role of Macroeconomic Indicators on Healthcare Cost.

The progress of healthcare expenses is a striking issue for emerging nations. This is because an uncontrolled increase in healthcare expenses can push the nations to extreme poverty. The study examined the association between public health costs and macro-economic indicators within the emerging economies. Data for the study is extracted from the World Bank World Development Indicators for twenty-one (21) emerging countries spanning from 2000 to 2018. The generalized method of moments (GMM) and the Dumitrescu-Hurlin panel causality test are employed in the analysis of the study. The main findings of the study demonstrate that tax revenue and labor force participation increase public health expenses and inflation, on the other hand, showed a declining relationship. The study further reveals a u-shaped association between public health expenditure and economic growth. The interactive term between research and development and mortality rate of non-communicable diseases, reveal an increasing relationship. The study establishes that, among all the three models estimated, tax revenue, labor force participation and GDP per capita have positive effects on public health costs. Based on the findings, the study recommends governments to embark on policies that improve economic growth and tax revenue as well as stabilizing inflation. These strategic policies could boost public healthcare expenditure since it has a strong association with macroeconomic indicators.

Empirical Analysis of Factors Influencing Healthcare Efficiency among Emerging Countries.

Numerous factors, including the inefficiency of healthcare resources have been attributed to the poor health outcome. The study aims to compare the efficiency of health expenses and its determining factors in the emerging economies based on their income levels. Data for the study is extracted from the World Bank's World Development Indicators for 21 countries covering the period of 2000 to 2018. Analysis of the research involves two stages. Stage one computes the efficiency scores, whereas second stage examines factors affecting health efficiency by employing the Tobit regression and Simar-Wilson regression test to confirm the results. The Tobit result shows that research and development (R&D) and physicians enhanced health efficiency at the main panel, lower-middle-income, upper-middle-income, and high-income countries. Corruption remained negative with education showing mixed results. The interaction between research and development and physicians increases health efficiency in all the panels. Health research must be a policy focus if efficiency is to be achieved by the emerging economies.

Different impacts of export and import on carbon emissions across 7 ASEAN countries: A panel quantile regression approach.

ASEAN (Association of Southeast Asian Nations) has contributed numerous carbon emissions during the phase of industrialization. This study mainly compares the different effects of export and import on CO2 emissions across 7 ASEAN countries over 1990-2017. In addition, we investigate how technological innovation affects carbon emissions. Stationary tests are conducted through cross section dependence, unit root of panel data, and Westerlund cointegration. The results of panel quantile regression show that export and import both have adverse effects on CO2 emissions. EKC is valid in these countries. Moreover, population size and energy intensity increase carbon emissions. In particular, technology innovation significantly reduces carbon emissions by augmenting energy efficiency. It is important to improve eco-innovation, and expand knowledge-intensive industries in ASEAN countries.

Innovation and CO2 emissions: the complimentary role of eco-patent and trademark in the OECD economies.

Increasing global emissions has led to research on the role of innovations play combating emissions. Mitigations from innovation perspective have mainly been focused on the role of patent, ignoring the role of trademarks. We therefore investigate the mitigating power of patent and trademarks in the OECD economies, benchmarking patent as the traditional mitigation strategy. Examining the complimentary role, we created an interaction term between patent and trademark. Our study divided the OECD economies into four subpanels which are OECD America, OCED Asia, OECD Europe, and OECD Oceania. We employed the Im, Pesaran and Shin W-stat, Augmented Dickey-Fuller, and Phillips Perron unit root tests, as well as cross-sectional dependence and Westerlund cointegration tests for the preliminary test on the variables. We also adopted ARDL approach to cointegration, Granger causality test, and OLS in examining the relationship between CO2 and patent, trademark, urbanization, and economic growth. Findings show that jointly, eco-patents and trademarks mitigate CO2 emissions. Also, bidirectional or unidirectional causal relationship was established between our variables of study, an indication that most of our variables can be used in forecasting one other.

The effects of economic growth and innovation on CO2 emissions in different regions.

Economic growth and economic energy consumption have received greater attention due to its contribution to global CO2 emissions in recent decades. The literature on CO2 emissions and innovation for regional differences is very scanty as there is not enough study that considered different regions in a single analysis. We adopt a holistic approach by incorporating different regions so as to assess how innovation contributes to emission reduction. The study, therefore, examined the effects of innovation and economic growth on CO2 emissions for 18 developed and developing countries over the period of 1990 to 2016. The study used panel technique capable of dealing with cross-section dependence effects: panel cross-sectional augmented Dickey-Fuller (CADF) unit root to determine the order of integration, Westerlund cointegration tests confirmed that the variables are co-integrated. We employed panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) to estimate the long-run relationship. The results show that energy consumption increases CO2 emissions at all panel levels. However, innovation reduces CO2 emissions in G6 while it increases emissions in the MENA and the BRICS countries. Environmental Kuznets curve (EKC) hypothesis is valid for the BRICS. The pollution haven hypothesis (PHH) and pollution halo effect were confirmed at different panel levels. Based on the findings different policy recommendations are proposed.

The effect of innovation on CO2 emissions of OCED countries from 1990 to 2014.

Human activities are accelerating CO2 emissions all over the world most especially in high-income nations, spurring the rise in greenhouse gas emissions. For decades, technologies have been developed and patented in response to the environmental problems. There is an outcry for innovative ways to combat the environmental menace. This attests to the enormity of research being done, in recent years, to investigate how innovation can help mitigate CO2 emissions. This research aims at investigating into the effect of innovation on CO2 emissions in 28 OCED countries at an individual level for the recent period 1990 to 2014. The source of data for our utilized variables is the World Bank Indicators. Our study employed three key models based on the STIRPAT model, the economic-EKC growth model, and the innovation-EKC model. The findings of our study revealed that innovation plays a key role towards mitigation of CO2 emissions in most OECD countries. Its impact, however, varies across the countries, depending on some key factors and channels elucidated in this paper. Additionally, our study asserts that improvement in GDP per capita leads to the rise in CO2 in most OECD economies, although mitigate emissions in few OECDs; hence, the economic-EKC model is not valid for most economies. Non-renewable energy accelerates emissions whiles renewable energy sources mitigate emissions. Research and development (R&D) improves environmental quality and the EKC for both economic growth and innovation, valid for a few economies of the OECDs. We conclude that innovation is necessary in mitigating CO2 emissions; hence, governments and policy makers should invest and promote innovative renewable energy sources.