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Learning-based analysis of the impact of environmental attention on food industry productivity

Year: 2024

Type: article

Abstract: This paper employs the machine learning method to identify the effect of the government’s environmental attention on Chinese food industry productivity. Using the China Industrial Enterprises Dataset and Government Work Report data from 2004 to 2013, we combine sentiment analysis, causal forests and deep neural networks to evaluate the effect of environmental attention on firm-level Total Factor Productivity (TFP) in the Chinese food industry. Results show that the government’s environmental attention has a positive impact on food firms’ productivity, which arises from the direct effects of firms’ reactions to environmental attention, as well as the indirect effects resulting from agricultural advancement. This paper fully integrates machine learning and natural language processing methodologies, providing a new perspective for social science research.

Source: The International Food and Agribusiness Management Review

DOI: 10.22434/ifamr.1128

Authors: Zimeng Chen, Shouhan Dai, Binlei Gong

Institution: Zhejiang University

Language: English

Topic: Environmental Sustainability in Business

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,078