Analyzing Nexus between Economic Complexity, Renewable Energy, and Environmental Quality in Japan: A New Evidence from QARDL Approach

The economic complexity index is an effective dimensionality reduction tool that is applied to forecast and predict future economic growth, income, and environmental quality. Renewable energy plays an important role in mitigation of carbon dioxide emissions. This study explores the nexus between economic complexity, renewable energy, FDI, trade, and environmental quality in Japan for the period 1970Q1-2019Q4. We use carbon dioxide (CO2) emissions as dependent variable while economic complexity index (ECI), foreign direct investment (FDI) inflow, renewable energy (RNE), and trade as explanatory variables. This study applies a quantile autoaggressive approach for analysis; the result of this study suggests a long-run implication of the ECI, FDI, GDP, RNE, and trade for the CO2 emissions. While only RNE and trade show mixed results in the short run, the rest of the variables do not have short-run implications. This implies that emissions mostly result in the industrial production activities only in the long run and in some quantiles only in the short run. The Japanese government may adopt different measures to reduce the CO2 emissions in the country, such as carbon tax and tax exemption on renewable energy investment. Furthermore, the government may adopt the renewal energy in production, which could achieve sustainable development goal

Analysis of the Spatiotemporal Changes in Watershed Landscape Pattern and Its Influencing Factors in Rapidly Urbanizing Areas Using Satellite Data

Analyzing the spatiotemporal characteristics and causes of landscape pattern changes in watersheds around big cities is essential for understanding the ecological consequence of urbanization and provides a basic reference for the watershed management. This study used a land-use transition matrix and landscape indices to explore the spatiotemporal change of land use and landscape pattern over Liuxihe River basin of Guangzhou in the southeast of China from 1980 to 2015 with multitemporal Landsat satellite data in response to the rapid urbanization process. Primary temporal and spatial influencing factors were first quantitatively identified through grey relation analysis (calculating correlation degree between land use changes and influencing factors) and Geodetector (detecting landscape spatial heterogeneity and its driving factors), respectively. Considerable spatial and temporal differences in land use and landscape pattern changes were observed herein, thus determining the influencing factors of these differences in the Liuxihe River basin. These changes were characterized by a large increase in construction land converted from cropland, particularly in the middle and lower reaches of the basin from 2000 to 2010, causing dramatic fragmentation and homogenization of the landscape pattern there. Meanwhile, the landscape pattern gradually transitioned from an agricultural land use dominant landscape to a construction land use dominant landscape in these regions. Furthermore, the rapid growth of a nonagricultural population and the transformation of industry primarily caused the temporal changes of landscape pattern, and the landscape spatial heterogeneity was mainly caused by the interaction of complicated geomorphology and anthropogenic activities in different spatial locations, particularly after 2000. This study not only provides an improved approach to quantifying the main spatiotemporal influencing factors of landscape pattern changes during different time periods, but also offers a reference for decision-makers to formulate optimal strategies on ecological protection and urban sustainable development of different regions in this study area