This paper investigates the relationship between technological progress in the energy sector and carbon emissions based on the Environment Kuznets Curve (EKC) and data from China during the period of 1995–2012. Our study confirms that the situation in China conforms to the EKC hypothesis and presents the inverted U-curve relationship between per capita income and carbon emissions. Furthermore, the inflection point will be reached in at least five years. Then, we use research and development (R & D) investment in the energy industry as the quantitative indicator of its technological progress to test its impact on carbon emissions. Our results show that technological progress in the energy sector contributes to a reduction in carbon emissions with hysteresis. Furthermore, our results show that energy efficiency improvements are also helpful in reducing carbon emissions. However, climate policy and change in industrial structure increase carbon emissions to some extent. Our conclusion demonstrates that currently, China is not achieving economic growth and pollution reduction simultaneously. To further achieve the goal of carbon reduction, the government should increase investment in the energy industry research and improve energy efficiency.

The adoption of automatic warehousing systems, a type of green technology, has been an emerging trend in the logistics industry. In this study, we develop a conceptual model using a technology–organization–environment framework to investigate the factors which influence logistics firms to adopt green technology. Our model proposes that the adoption of green technology is influenced by perceived advantage, cost, technological turbulence, business partner influence, firm size, firm scope and operational performance. The objective of this study is to identify the conditions, as well as the contributing factors, for the adoption of automatic warehousing systems in logistics firms. Data were collected from 98 firms in China, and structural equation modeling with partial least squares is adopted to analyze the data. The results suggest that high perceived relative advantage, firm size, cost, firm scope, operation performance, technological turbulence and influence of business partners are important factors affecting IT adoption in small businesses. Therefore, decision support should be provided for enterprises from the three aspects of technology, organization and environment to improve the adoption of automatic warehousing systems.