Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

A current and universal challenge, particularly in developing nations, is the establishment of effective environmental regulation policies that protect the ecological environment without adversely affecting the international competitiveness of the domestic manufacturing industry. To deal with this dilemma, this study investigates the export competitiveness of China’s manufacturing industry from the viewpoint of export value added. The Porter hypothesis is applied for an empirical investigation of the effect of environmental regulation on export competitiveness and to determine the presence of intra-industry heterogeneity. Furthermore, this study seeks to understand the mechanisms through which environmental regulation affects export competitiveness by exploring the two main approaches to technological innovation. The findings reveal that environmental regulation has a promotion effect of approximately 2% on the export competitiveness of China’s manufacturing industry; however, this effect is non-linear and displays a “U-shaped” tendency, indicating that certain prerequisites must be fulfilled to validate the Porter hypothesis. In addition, the effect of environmental regulation displays significant intra-industry heterogeneity, which is evident primarily in heavily polluting sub-industries and to a lesser extent in moderately polluting sub-industries but insignificant in lightly polluting sub-industries. Environmental regulation also differs significantly in the mechanisms through which it affects different approaches to technological innovation. Independent research and development is affected by environmental regulation through the compliance cost effect, which limits export competitiveness, while technology introduction is affected by the innovation offset effect, which favors export competitiveness. These findings offer political implications for the sustainable development of the ecological environment and foreign trade.

Section: Benchmark Regression Resultsmentioning

confidence: 99%

Environmental Regulation, Technological Innovation, and Export Competitiveness: An Empirical Study Based on China’s Manufacturing Industry

Liu

Xie

2020

“…To address this problem, we use R&D efficiency to approximate the product productivity because R&D expenditure is an important part of enterprises' fixed expenditures. The higher the R&D efficiency, the higher the product productivity of enterprises [43], as there is a strong positive correlation between the two.…”

Section: Mediating Variablementioning

confidence: 98%

Does Environmental Regulation Affect Export Quality? Theory and Evidence from China

Xie

Sun

Wang

et al. 2020

Self Cite

Most studies focus on the empirical investigation of the relationship between environment and trade, but they lack a systematic theoretical framework. To fill this gap, this study constructs an analytical framework of export competitiveness from the perspective of product quality, and reveals the theoretical mechanism of environmental regulation affecting export quality. We empirically examine the impact of environmental regulation on the export quality of China’s manufacturing industry, as well as its possible mechanism. Our findings show that environmental regulation can significantly promote the export quality upgrading of the manufacturing industry and that process and product productivity are two possible channels through which such regulation affects export quality, although their mediating effects are in opposite directions. The mediating effect of product productivity is greater than that of process productivity, indicating that environmental regulation mainly has an innovation offset effect on China’s manufacturing industry. For pollution-intensive industries, environmental regulation plays a significant promoting role through the channel of product productivity, but, for clean industries, environmental regulation has an inhibitory effect through the channel of process productivity. These findings provide important enlightenment for the coordinated development of China’s ecological civilization and trade power.

“…Referring to the measurement method of output density model [ 39 ], this measurement of innovation agglomeration can describe the spatial density and distribution of innovation activities. Patent data are often used because of their palpable link with innovation output and their relative integrity [ 68 ]. In this paper, the number of patents granted (pieces) is selected to represent regional innovation output.…”

Section: Data Description and Empirical Modelmentioning

confidence: 99%

Can Innovation Agglomeration Reduce Carbon Emissions? Evidence from China

Zhang

Liang

et al. 2021

Self Cite

Innovation agglomeration plays a decisive role in improving the input–output scale and marginal output efficiency of factors. This paper takes carbon emissions as the unexpected output and energy consumption as the input factor into the traditional output density model. The dynamic spatial panel Durbin model is used to analyze the mechanism for innovation agglomeration and energy intensity to affect carbon emissions from 2004 to 2017 in thirty Chinese provinces. Then, we test the possible mediating effect of energy intensity between innovation agglomeration and carbon emissions. The major findings are as follows. (1) The carbon emission intensity has time-dependence and positive spatial spillover effect. That is, there is a close correlation between current and early carbon emissions, and there is also a high-degree correlation between regional and surrounding areas’ carbon emissions. (2) Carbon emissions keep a classical inverted U-shaped relation with innovation agglomeration, as well as with energy intensity. However, the impact of innovation agglomeration on carbon emissions in inland regions of China does not appear on the right side of the inverted U-shaped curve, while carbon emissions are subject to a positive nonlinear promoting effect from energy intensity. (3) When the logarithm of innovation agglomeration is more than 3.0309, it first shows the inhibition effect on energy intensity. With the logarithm of innovation agglomeration exceeding 5.0100, it will show the dual effect of emission reduction and energy conservation. (4) Energy intensity could work as the intermediary variable of innovation agglomeration’s influence on carbon emissions. Through its various positive externalities, innovation agglomeration can produce a direct impact on carbon emissions, and through energy intensity, it can also affect carbon emissions indirectly.