Re-Examination of the Relationship between Agricultural Economic Growth and Non-Point Source Pollution in China: Evidence from the Threshold Model of Financial Development

Abstract

The coordinated development of agricultural economic growth and non-point source (NPS) pollution is an important task in enhancing pollution prevention. Significantly, agricultural economic growth and NPS pollution are interrelated, and their mechanism will be affected by financial development. For this reason, the current study established a panel smooth transformation regression (PSTR) model to reveal the mechanical evolution under different financial development levels. It was found that the impact of agricultural economic growth on NPS pollution was significantly positive in the low level of financial development, which is manifested as an “intensification effect”. Fortunately, when the level of financial development reaches the medium and high thresholds, agricultural economic growth will inhibit agricultural NPS pollution. At the same time it was also found that the impact of agricultural economic growth on NPS pollution is manifested as an “inhibition effect” at the overall level, but presenting significant structural differences. Specifically, the impact on the eastern and central regions of China is manifested as an “inhibition effect”, whereas the impact in the western region of China is characterized as an “intensification effect”. Finally, the elasticity analysis showed that the influence of financial development on agricultural NPS pollution was significantly positive, and that its intensification effect is ubiquitous.

Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China from the Perspective of Sustainable Development

“…The following abbreviations are used in this manuscript: NCER (Northeast Comprehensive Economic Region); NCCER (Northern Coastal Comprehensive Economic Region; ECCER (Eastern Coastal Comprehensive Economic Region); SCER (Southern Coastal Economic Region); YRCER (Yellow River Basin Comprehensive Economic Region); YRBCE (Yangtze River Basin Comprehensive Economic Region); GSCER (Great Southwest Comprehensive Economic Region); GNCER (Great Northwest Comprehensive Economic Region). Province numbers: Liaoning (6), Jilin (7), Heilongjiang (8), Beijing (1), Tianjin (2), Hebei (3), Shandong (15), Shanghai (9), Jiangsu (10), Zhejiang (11), Fujian (13), Guangdong (19), Hainan (21), Shaanxi (27), Shanxi (4), Henan (16), Inner Mongolia (5), Hubei (17), Hunan (18), Jiangxi (14), Anhui (12), Yunnan (25), Guizhou (24), Sichuan (23), Chongqing (22), Guangxi (20), Gansu (28), Qinghai (29), Ningxia (30), Tibet (26), Xinjiang (31).…”

“…As a comprehensive and systematic endeavor, agricultural green development encompasses a wide range of areas. Gaining a comprehensive and systematic understanding of the state of agricultural green development, is an important prerequisite and foundation for promoting its progress [7]. Measuring and analyzing the level of agricultural green development aids in monitoring and evaluating the environmental impact of agricultural activities while ensuring the sustainability of agricultural systems.…”

[Retracted] Empirical Study on the Relationship between Agricultural Economic Structure Growth and Environmental Pollution Based on Time‐Varying Parameter Vector Autoregressive Model

“…It is difficult to achieve a harmonious development between environment, resources, and economic development, and the contradiction between the two is increasingly prominent. Based on the perspective of China's industrial development, since the 1980s, up to now, the growth rate of industrial wastewater discharge has shown a relatively low degree of decline [ 2 ]. However, in addition, the discharge of industrial solid pollutants and industrial waste gas both showed an increase, with an average increase of 6.36% and 8.49% per year, respectively.…”

The Effect of Farmland Transfer on Agricultural Green Total Factor Productivity: Evidence from Rural China

“…The existing studies on farmland transfer have mainly focused on the relationship between farmland transfer and economic and environmental variables, such as household income [13], poverty vulnerability [14], allocation efficiency of agricultural land [15], and environmental performance [16][17][18]. Li et al (2019) [13] used fuzzy mathematical methods to estimate the welfare gap before and after farmland transfer in Guangzhou, Wuxi, and Chongqing, finding that the overall welfare of farmers after farmland transfer increased by 17.5%, 15.1%, and 23.5%, respectively.…”

“…However, some scholars put forward the opposite view. Jiang et al (2021) [18] point out that moderatescale operations did not meet the policy expectation of curbing agricultural nonpoint source pollution; [20] also found that farmland transfer improved the overall nonpoint source pollution level of the transferred farmland.…”