Can technological learning significantly reduce industrial air pollutants intensity in China?—Based on a multi-factor environmental learning curve

Abstract

Abstract This paper introduces a methodology to examine the effect of technological learning on industrial air pollutants intensity in China. It is based upon the theory of learning curves and environmental learning curves. In this way it is possible to estimate the effect of technological learning on industrial air pollutants intensity in two steps. First, we build a multi-factor environmental learning curve model to investigate the relationship between technological learning and industrial air pollutants intensity. At the second stage of the analysis, we assess whether and how different types of technological learning can reduce industrial air pollutants intensity based on a mediating effect model. The results show that there is a causal relationship between technological learning and industrial air pollutants intensity. Moreover, different types of technological learning have the same influence way on industrial air pollutants intensity: learning-by-doing can significantly reduce industrial air pollutants intensity through energy efficiency; and the same is true with learning-by-researching and learning-by-importing. In addition, different types of technological learning have different influence degree on industrial air pollutants intensity: learning-by-doing is the leading contributor to the reduction in industrial air pollutants intensity, while the contribution of learning-by-researching is the smallest.