Modelling Technological Bias and Productivity Growth: A Case Study of China’s Three Urban Agglomerations

Abstract

The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s environmental problems. This study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP) index of the Chinese three urban agglomerations from 2005 to 2014, and the reasons for its changes are also analyzed. Furthermore, the biases of technological progress from two perspectives of inputs and outputs (including the undersirable output, measured by CO2 emissions) are estimated. Main results are: (i) During the sample period, the TFP of the three urban agglomerations continues to increase, and the main driving force is technological change. (ii) From the perspective of inputs, the Beijing-Tianjin-Hebei prefers to use electricity, whereas the Pearl River Delta and the Yangtze River Delta urban agglomerations tend to use capital and save labor. (iii) From the perspective of outputs, the technological progress of the three major urban agglomerations is significantly biased toward GDP with a slight difference among the three urban agglomerations, which means its technological progress is conducive to reduce CO2 intensity, symbolizing low carbon development. From this point of view, their economic growth shows a low-carbon trend.

Explore the key factors of sustainable development: A bibliometric and visual analysis of technological progress

“…Based on the multiinput multioutput perspective, scholars such Bosetti et al (2009), Cheng et al (2021), Fisher‐Vanden et al (2006), Ren (2021) have introduced the factor of energy into their research models. In addition, several scholars such as Li et al (2020), Manne and Richels (2004), Popp (2004) have incorporated carbon emissions into the output factor indicator construction system to investigate low‐carbon technological progress bias and to verify the effect of input factor prices and elasticity of substitution on technological progress bias. This stage saw an improvement in the measurement of biased technological progress.…”

“…Therefore, some scholars have chosen to adopt the SFA method to measure technological progress bias when working with multiple factor input models (Cheng et al, 2019;Karanfil & Yeddir-Tamsamani, 2010;Shao & Wang, 2016). In addition, greenbiased technological progress can be measured by constructing new DEA models, such as the DEA-Malmquist index model based on using the input factors of energy, capital, and labor (Li et al, 2020;Ramanathan, 2005). Based on the foregoing analysis, this article makes prospects from the aspects of research perspective, theme and content, so as to provide a reference basis for promoting scientific decision-making of sustainable development.…”

“…Klump et al (2007Klump et al ( , 2008 empirically measured the level of technological progress in the United States and Europe in 1970 using a standardized supply side systems approach. Li et al (2020) empirically tested the impact of technological progress on low-carbon development in China based on the Malmquist-Luenberger index. In recent years, as the consumption of natural resources intensifies and the deterioration of the environment accelerates, "green" technological progress, which emphasizes environmental protection, has received attention from scholars.…”

The Regional Disparity of Influencing Factors of Technological Innovation in China: Evidence From High-Tech Industry

“…At present, China's economy enters the "new normal", "innovation-driven growth" is the key to China's future low carbon development, so stimulating the enterprise innovation consciousness to promote economic development through technological progress is the new driving force of China's economy (Li et al, 2020;Xiao et al, 2018;Noesselt, 2017). When the core mechanisms behind technological innovation in different regions is accurately analyzed and grasped, Chinese governments can make policies and countermeasures according to the geographical location to promote technological innovation, economic transformation and upgrading.…”

Assessing Agricultural Malmquist Total Factor Productivity and Environmental Efficiency for Some Arab Countries Via Data Envelope Analysis (A Non Parametric Approach) تقدير مؤشر مالمکويست للإنتاجية الکلية للعوامل الزراعية و الکفاءة البيئية لبعض الدول العربية مستخدماً طريقة مغلف البيانات: المنهج الا معلمى

“…Most of these studies have been performed at regional and farm level, such as Reinhard et al (2002) for dairy farms in the Netherlands, Coelli et al (2007) for pig farms in Belgium, Abedullah et al (2010) for rice farms in Pakistan. Thanh Nguyen et al (2012) has also been used in South Korea, Kuo et al (2014) in Taiwan, Marchand and Guo (2014), Li, K et al (2020) modeling technical bias and productivity growth in China and Tu et al (2015) in Vietnam. Other studies performed cross-country analyzes for agricultural TFP and environmental efficiency, such as Hoang and Coelli (2011) and estimates for 30 OECD member countries for the period 1990, and Moreno-Moreno et al (2017 assessed the operating efficiency for 18 Latin American and Caribbean countries for 2012.…”