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This paper aims to provide a typical example of accounting for the carbon dioxide equivalent (CO₂e) in underdeveloped cities, especially for the Poyang Lake area in China. The accounting can increase public understanding and trust in climate mitigation strategies by showing more detailed data.

The paper uses the “Global Protocol for Community-scale greenhouse gas emission inventories (GPC)” method, a worldwide comparable framework for calculating urban CO₂e emission (CE). The empirical case is an underdeveloped city, Nanchang, in China.

The results show the total CE of Nanchang, containing the electricity CE of Scope 2, grew rapidly from 12.49 Mt in 1994 to 55.00 Mt in 2014, with the only recession caused by the global financial crisis in 2008. The biggest three contributors were industrial energy consumption, transportation and industrial processes, which contributed 44.71-72.06, 4.10-25.07 and 9.07-22.28 per cent, respectively, to the total CE. Almost always, more than 74.41 per cent of Nanchang’s CE was related to coal. When considering only the CEs from coal, oil and gas, these CEs per unit area of Nanchang were always greater than those of China and the world. Similarly, these CEs per gross domestic product of Nanchang were always bigger than those of the world. Thus, based on these conclusions, some specific countermeasures were recommended.

This paper argues that the CO₂e accounting of underdeveloped cities by using the GPC framework should be promoted when designing climate mitigation policies. They can provide more scientific data to justify related countermeasures.

The purpose of this paper is to analyze the impact of logistics performance on the carbon (CO2) emissions of Balkan countries.

Fixed-effects panel regression analysis is used to estimate the causal relationship between CO2 emissions and logistic performances of Balkan countries. Logistics performance is measured by logistics performance index (LPI) which was published by the World Bank in 2007, 2010, 2012, 2014 and 2016 and used for ranking countries by means of their logistics performance. LPI is based on six main indicators: customs procedures, logistics costs and the quality of the infrastructure for overland and maritime transport. As a measure of carbon emissions of sampled countries, the natural logarithm of carbon dioxide emission per capita is used in this study.

The results obtained reveal that there is a positive and significant relationship between logistics performance and CO2 performances of the sampled Balkan countries.

This study is based on only 11 Balkan countries. In this sense, the data used in the analysis is limited.

Considering the important geostrategic position of the Balkan region, logistics sector has an important role for the development of the countries in that region. In this sense, the findings of this study may provide useful insights for policymakers to achieve sustainable economic development. Furthermore, as far as the authors know, this is the first study that focuses on the relationship between logistics performance and carbon emissions of Balkan countries.

The purpose of this paper is to develop an integrated theoretical framework for energy security concept and to shed light on the policies and strategies applied by the European Union countries to confront the challenges that faces them.

The research paper uses Regional Security complexes theory, which mainly developed in Copenhagen school for security studies, that founded by Barry Buzan. This school tried to clarify the untraditional security aspects, through expanding its scope by adding new dimensions than military perspective.

Despite the consolidated efforts exerted by the European Union to assure safe levels of energy security, and their continuous pursuit to be liberated from Russian energy over dependence, but the results are still limited.

The value of this research paper stems from the fact that it encompass the theoretical aspect by shedding light on all the developments occurred to energy security concept, in addition to the Empirical side, by analyzing various European energy security challenges and their confrontation strategies.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

This study aims to examine the impacts of climate change (CC), measured average annual rainfall, average annual temperature and carbon dioxide (CO₂e) on cereal production (CPD) in Bangladesh by using the annual dataset from 1988–2014, with the incorporation of cereal cropped area (CCA), financial development (FD), energy consumption (EC) and rural labor force as important determinants of CPD.

This study used an auto-regressive distributive lag (ARDL) model and several econometric approaches to validate the long- and short-term cointegration and the causality directions, respectively, of the scrutinized variables.

Results of the bounds testing approach confirmed the stable long-term connections among the underlying variables. The estimates of the ARDL model indicated that rainfall improves CPD in the short-and long-term. However, CO₂e has a significantly negative impact on CPD both in the short-and long-term. Results further showed that temperature has an adverse effect on CPD in the short-term. Among other determinants, CCA, FD and EC have significantly positive impacts on CPD in both cases. The outcomes of Granger causality indicated that a significant two-way causal association is running from all variables to CPD except temperature and rainfall. The connection between CPD and temperature is unidirectional, showing that CPD is influenced by temperature. All other variables also have a valid and significant causal link among each other. Additionally, the findings of variance decomposition suggest that results are robust, and all these factors have a significant influence on CPD in Bangladesh.

These findings have important policy implications for Bangladesh and other developing countries. For instance, introduce improved cereal crop varieties, increase CCA and familiarizes agricultural credits through formal institutions on relaxed conditions and on low-interest rates could reduce the CPD’s vulnerability to climate shocks.

To the best of the authors’ knowledge, this study is the first attempt to examine the short- and long-term impacts of CC on CPD in Bangladesh over 1988–2014. The authors used various econometrics techniques, including the ARDL approach, the Granger causality test based on the vector error correction model framework and the variance decomposition method.

This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.