Analysis of the scientific and technological innovation efficiency and regional differences of the land–sea coordination in China's coastal areas

Wei

et al. 2021

Growth and Change

The city is both a carrier and a subject of innovation. Based on the triple helix theory of industry–university research and the theory of spatial correlation, this study constructs a collaborative innovation framework both within the cities and between cities, and uses a network data envelopment analysis (DEA) model and spatial econometric model to measure and analyze the collaborative innovation efficiency in 75 innovative cities in China. The results show that collaborative innovation efficiency within cities is on the rise, and the efficiency of “research to production” is significantly higher than that of “learning to research.” Industrial structure and foreign factors have inhibited the efficiency improvements, and infrastructure and living standards have different promoting effects on different stages of efficiency. Between cities, capital flows have obvious spillover effects, which promote the efficiency of innovation networks, while the long‐term characteristics of institutional learning have a near‐term negative impact.

Section: Literature Reviewmentioning

confidence: 99%

Section: Innovation Evaluation: From Index Framework To Deamentioning

confidence: 99%

Collaborative innovation efficiency: From within cities to between cities—Empirical analysis based on innovative cities in China

Wei

et al. 2021

Growth and Change

“…Then, a VAR model was used to explore the pulse response relationship between marine industrial structure and marine economic efficiency, with the advancement of the marine industrial structure advancement significantly improving marine economic efficiency. Xia et al (2019) used the GML index to measure the marine economic efficiency of 11 coastal areas in China from 2006 to 2015 and studied the determinants of the marine economy. Their research results showed that at the level of land economic development, marine industrial structure, personnel structure of science and technology systems, production status and government intervention all had significant effects on marine economic efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of marine industrial structure on environmental efficiency

Xing

Chen

2020

MEQ

Purpose Recently, China has been paying increasing attention to how to improve the efficiency of the marine environment and realize a green and sustainable development of the marine economy. Consequently, the industrial structure is crucial to improving efficiency. The purpose of this paper is to introduce environmental factors into the efficiency analysis framework and explore the relationship between marine industrial structure and marine environmental efficiency. Design/methodology/approach This paper uses marine economic data under the DEA-BBC model to measure the marine environmental efficiency of provinces and cities and classifies them by cluster analysis. Then, the marine industrial structure and marine environmental efficiency are studied by an econometric model with human capital, ownership structure, land economic development level, scientific research input and government intervention degree as control variables. Findings The overall level of marine environmental efficiency is relatively low in China, increasing and then decreasing over the research period. The rationalization of industrial structure and scientific research input have significant promoting effects on marine environmental efficiency, while the degree of government intervention has a significant inhibiting effect. The positive effect of human capital on efficiency depends on whether it can be successfully converted into productivity. The effects of industrial structure advancement, ownership structure and land economic development level of on the marine environmental efficiency are mixed. Originality/value The results provide a theoretical and decision-making basis for China to transform and upgrade its marine industrial structure and sustainably develop the marine economy.

“…The GMLI was first proposed by Oh [59] as an alternative to an environmentally sensitive productivity growth index, which can estimate the dynamic change of CEP over time. Since then, the GMLI has been widely applied in different areas [60,61]. The GMLI equation is defined below.…”

Section: Global Malmquist-luenberger Index (Gmli)mentioning

confidence: 99%

The Assessment of Carbon Performance under the Region-Sector Perspective based on the Nonparametric Estimation: A Case Study of the Northern Province in China

et al. 2019

Sustainability

China is the largest emitter of carbon dioxide (CO2) in the world, and the Chinese government has accordingly proposed a series of measures to achieve a low-carbon economy. Due to the low carbon emission performance (CEP) and the high industry portion of the northern provinces in China, evaluating the CEPs of industrial sectors in northern China is necessary. By considering the different CEP assessments in regional and industrial research, a dual-perspective assessment of CEP was presented to narrow the gap between the regional and industrial perspectives. The dual model of slack-based measure (SBM) and data envelopment analysis (DEA) was combined with the global Malmquist–Luenberger index (GMLI) proposed to measure the static CEP and the dynamic change of the CEP of six provinces in northern China from 2006–15 for the regional and industrial perspectives, respectively. A comparison of the results under the different perspectives proved the irrationality of our evaluation under the sole perspective. For example, for Jilin Province, the CEPs of Mining and Processing of Nonmetal and Other Ores (Sector 4) ranked in the top 30% in the regional perspective. However, in the industrial level, the CEPs of Mining and Processing of Nonmetal and Other Ores (Sector 4) ranked lower. The CEPs of the Production and Supply of Electric Power and Heat Power (Sector 20) of Heilongjiang Province ranked in the bottom 30% in a regional perspective but ranked first at the industrial level. We also found the advantage sectors in the CEP under the region–sector dual perspective. For example, for Jilin Province, the Processing of Petroleum, Coking, and Processing of Nuclear Fuel (Sector 10) and the manufacture of Transport Equipment (Sector 16) were the advantageous sectors. The dual-perspective assessment aimed to evaluate the CEP under diverse views. It also provided a more reliable path to reduce CO2 emissions for managers and regulators.