Liying Li’s research while affiliated with Henan University of Technology and other places

Faced with the pressure of slowing industrial growth and industrial transformation requirements, it is crucial to analyze the changes and the corresponding driving factors of the food processing industry in China. An analysis using traditional and spatial shift‐share models was conducted to decompose the changes in the food processing industry in each region of China from 2009 to 2019 into five effects: national growth effect (NG), industrial mix effect (IM), competitive effect (CE), neighbor‐nation competitive effect (NNC), and region‐neighbor competitive effect (RNC). Among the five effects from 2009 to 2019, the NG contributed the most to the growth in most regions, indicating that the development of the food processing industry in China was greatly influenced by the industrial base and that China's food processing industry has entered a “growth bottleneck period.” During the period 2009–2014 to period 2014–2019, compared to the IM and CE, the influence of spatial spillover effects was stronger and significantly enhanced. Moreover, the IM, CE, NNC, and RNC in most southern regions were stronger than those in most northern regions. Therefore, China's food processing industry needs and is transforming into high‐quality development. It is necessary to innovate the mode of development of food processing industry and strengthen interregional exchanges and cooperation. An analysis using traditional and spatial shift‐share models was conducted to decompose the changes in food processing industry in each region of China from 2009 to 2019 into five effects. This study found that from 2009 to 2019, the output value growth of the food processing industry in most regions of China was most affected by the industrial base, followed by the spatial spillover effect, and the least by the industrial structure.

Continuous rise in a global economy with a 3–4% annual growth rate poses a severe risk to environmental sustainability due to high energy demand. Since the Paris climate accord, countries worldwide have implemented numerous strategies to attain the target of carbon neutrality. With the rising environmental challenges, it is important to consider global financial inclusion (F.I.) policies. This study uses panel data for the B.R.I.C.S. countries to investigate the impact of F.I. and energy efficiency in limiting trade adjusted emissions (T.A.E.) taking technological innovation and trade as control variables. This study uses panel data consisting small sample size and large time period; therefore, keeping in mind the potential econometric problems, this study uses AMG method, which can efficiently deal with endogeneity problems and small sample bias. We find a positive impact of F.I. and energy efficiency on CO2 emissions. Moreover, we find that technological innovation, exports and output amplify CO2 emissions.