Inclusive green growth (IGG) offers an effective alternative to pursue sustainable development. The core of the IGG system lies in the coordination of inclusive, green, and growth subsystems. However, there is little quantitative assessment on IGG based on subsystem collaboration. This study proposes a holistic scheme of inclusive-green-growth nexus in Anhui province from 2009 to 2018 by using an integrated approach, namely, the entropy weight approach (EWA), coupling coordination degree model (CCDM), grey prediction model (GPM), and obstacle factor diagnostic model (OFDM). The results show that: (1) The proposed integrated approach could be viable to measure the synergistic interactions among internal IGG subsystems; (2) At the provincial level, a relatively high IGG performance but a low coupling coordination degree (CCD) of the IGG nexus are seen. Although the predicted value of CCD will show an upward trend, it will not be able to cross the start stage. The obstacle factors on the coordinated development of IGG can be divided into two stages: (3) At the prefectural level, the cities in which CCD is rising outnumber those it is falling. However, the CCD is also low, and the gap between cities is getting wider. The obstacles that affect the CCD of cities see a dynamic evolution trend from “inclusive obstacle type” to “inclusive and growth obstacle type” then to “green obstacle type” over the decade.
Exploring the transformation of the mode of economic growth has been widely concerned by researchers around the world, especially in the context of “dual carbon” goals. China’s economy is in a transition stage to high-quality green development (HGD), with technological innovation (TI) and emerging industrial agglomeration (EIA) as important drivers. However, existing studies lack a systematic empirical analysis on different dimensions of TI and its influence mechanism. In order to examine the mechanism of TI affecting HGD through EIA, this study uses the panel data in the western Yangtze River Delta of China from 2009 to 2019 to first comprehensively calculate the TI index, EIA index, and the HGD index as proxy variables for regional TI, EIA, and HGD, respectively, and then empirically examines the impact of TI on HGD and the heterogeneous impacts on HGD. Finally, we test the mediation effect of EIA between TI and HGD. The results show that TI effectively promotes regional HGD, and with the increase in the HGD index, the driving role of TI also increases. Technological innovation has heterogeneous impacts on HGD at different dimensions of innovation, time periods, and regions. Innovation output and diffusion have significant impacts on HGD, whereas innovation input and environment have insignificant impacts. The effects of TI promoting HGD are different before and after 2013. Additionally, the nexus between TI and HGD also differs across spatial distributions. The mediation effect indicates that EIA is an important mechanism for TI to release the dividends of HGD, and it explains approximately 29% of the conduction effect.
High-quality economic development (HQED) has recently become a crucial sustainable growth mode in China, which pursues economic growth while maintaining social equity and green ecology. The HQED of the Yangtze River Delta (YRD) has played an exemplary role in achieving the leap from “China speed” to “China Quality”. In this paper, we first use the entropy-weight multidimensional comprehensive evaluation method to calculate the HQED index as a proxy for the quality of economic growth. Then, using panel data of 41 cities in the YRD, we conduct a comparative study to examine impacts of technological innovation (TI) on quantity and quality of economic growth by employing different panel estimation models over the period 2009-2019 and check the robustness in five ways. Finally, this paper investigates the TI-economic growth link based on the panel quantile regression across the conditional distributions of economic growth levels. Results show that TI has significantly positive effects in terms of both quantity and quality of economic growth, and the promoting effect on the quantity of economic growth is almost four times higher than that of quality under mean estimations by double fixed-effects. The effect on quantity of economic growth is also stronger than that of quality under the conditional distribution, and TI has a stronger impact for regions with higher levels of economic growth.
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