Do the National Key Ecological Function Zones Promote Green Development? Evidence from the Yanshan–Taihang Mountainous Area in Hebei Province, China

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Abstract

National key ecological function zones (NKEFZs) in China are critically important to maintain ecological security and accelerate the construction of the ecological civilization system. The quantitative impact of NKEFZs on green development at the county level has not received much attention. In this study, the county-level statistics of the Yanshan–Taihang Mountainous area in Hebei province from 2013 to 2018 were selected as samples. The symbiotic relationship between economy and ecology was measured and development patterns were identified using an improved Lotka–Volterra model. Then, a difference-in-differences (DID) model was used to empirically test the green development effects of establishing NKEFZs, as well as the dynamic changes of the effects and the heterogeneity of different development patterns. The results show that: (1) Green development in the sample area can be classified into four patterns: Low-High, High-Low, High-High, and Low-Low; (2) NKEFZs reduce the level of green development in the study interval and have a persistent negative effect on the level of green development; and (3) NKEFZs have a significant negative effect on areas of Low-High and High-Low development patterns, while the effect on areas of Low-Low development patterns is not significant. Overall, the results indicate that NKEFZs have a negative impact on the green development of the Yanshan–Taihang Mountainous area in Hebei Province. Finally, to promote green development in mountainous areas, the paper makes the following recommendations: Firstly, to improve the transfer payment system for NKEFZs and bring into play a long-term mechanism for the compensation effect of transfer payments. Secondly, to cultivate special industries to achieve the differentiated development of county economies. Thirdly, to accelerate the optimization and adjustment of industrial structures and promote the coordinated development of primary, secondary, and tertiary industries.

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Zhiyun Ouyang, Hua Zheng, Yi-An Xiao … (2016)

In response to ecosystem degradation from rapid economic development, China began investing heavily in protecting and restoring natural capital starting in 2000. We report on China's first national ecosystem assessment (2000-2010), designed to quantify and help manage change in ecosystem services, including food production, carbon sequestration, soil retention, sandstorm prevention, water retention, flood mitigation, and provision of habitat for biodiversity. Overall, ecosystem services improved from 2000 to 2010, apart from habitat provision. China's national conservation policies contributed significantly to the increases in those ecosystem services.

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Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.

Aaron van Donkelaar, Randall V. Martin, Michael Brauer … (2016)

We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R(2) = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m(3) WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.