Sustainable development through green innovation and resource allocation in cities: Evidence from machine learning

Abstract

China has promoted innovation-driven and green development to unprecedented strategic heights. However, compared to the large and rapid innovation investment, total factor productivity's (TFP) growth rate has shown a downward trend. Consequently, this study assesses the inefficiency caused by resource mismatch and discusses the impact of green innovation activities on green total factor productivity (GTFP). We use a causal forest-based machine learning method to solve the endogenous problem. The empirically analyzes the observation samples of 272 prefecturelevel cities in China from 2008 to 2018 and obtains the asymptotic normality estimation on the average treatment effect (ATE). Simultaneously, clustering causal forest and ridge expressions, discusses the robustness of related problems. According to the results, (1) the effect of China's green innovation on GTFP is negative for a short time and positive for a long time; (2) the impact of green innovation activities on GTFP is subject to capital mismatch, while the statistical law of the impact of labor mismatch is not obvious but the adverse impact of resource mismatch is gradually improving; and (3), Green innovation has significantly improved China's GTFP, but it did not lead to ideal Growth rate of GTC.

The relationship between green innovation and sustainable growth in Korean companies: Moderated mediation effect of ESG score by industry

“…This study deals with GI, ESG, and Sustainability Management, which are the most noteworthy corporate management issues in recent years. While there have been many studies showing that GI and ESG have a significant impact on firm value, financial performance, and sustainability, some researches have shown negative effects or inconclusive conclusions in such relationships (Su, 2019; Oprean‐Stan et al, 2020; Asadi et al, 2020; Duque‐Grisales et al, 2020; Zhang & Shuang, 2021; Xie et al, 2022; Steinhaeuser, 2022; Mao et al, 2023 etc.). In addition, concerns over environmental investment are rising and confidence in ESG evaluation has also fallen a lot (Earth.org, 2023).…”

“…Xie et al (2022) found that green innovation is negatively related to the firm value of China's heavy pollution enterprises but this devaluation effect occurs only in the short term. Mao et al (2023) found that China's growth rate of Total Factor Productivity (TFP) is on the decline despite large and fast-paced investment in green innovation.…”

“…Xie et al (2022) found that green innovation is negatively related to the firm value of China's heavy pollution enterprises but this devaluation effect occurs only in the short term. Mao et al (2023) found that China's growth rate of Total Factor Productivity (TFP) is on the decline despite large and fast‐paced investment in green innovation. To be more specific, they confirmed that the effect of China's green innovation on Green Total Factor Productivity (GTFP) is negative for a short time and positive for a long time ; green innovation has grown China's GTFP very significantly, but it has not led to the ideal growth rate of Green Technical Change (GTC).…”

Leveraging behavioral data mining for cost reduction and efficiency enhancement in manufacturing

“…As the economy rapidly expands and industrial enterprises grow in scale, canteen managers face the challenge of accurately predicting the number of diners and effi-ciently allocating resources to better meet the dietary needs of employees [1][2].…”

Can business managerial capacity improve green innovation in different industries? Evidence from Chinese listed companies

“…China's innovation‐driven development has been characterized by rapid progress despite complex challenges locally. As a key driver of coordinated economic‐and environmental development, GI has received much attention in recent years (Mao et al, 2023; Xie & Han, 2022). At the core of promoting green development and Beautiful China Initiative is the competition of green technological innovation (Liu et al, 2023; Zhao et al, 2022).…”