Integrating ecosystem services supply and demand into optimized management at different scales: A case study in Hulunbuir, China

et al. 2021

IJERPH

Shortfalls and mismatches between the supply and demand of ecosystem services (ES) can be detrimental to human wellbeing. Studies focused on these problems have increased in recent decades, but few have applied land use optimization to reduce such spatial mismatches. This study developed a methodology to identify ES mismatches and then use these mismatches as objectives for land use optimization. The methodology was applied to the Guangdong-Hong Kong-Macao “Greater Bay Area” (GBA), a megacity of over 70 million people and one of the world’s largest urban agglomerations. Considering the demand for a healthy and secure living environment among city-dwellers, we focused on three ES: heat mitigation, flood mitigation, and recreational services. The results showed large spatial heterogeneity in supply and demand for these three ES. However, compared to current conditions in the GBA, our model showed that optimized land use allocation could better match the supply and demand for heat mitigation (number of beneficiaries increased by 15%), flood mitigation (amount of population exposed to flood damage decreased by 37%), and recreation (number of beneficiaries increased by 14%). By integrating land use allocation and spatial mismatch analysis, this methodology provides a feasible way to align ES supply and demand to advance urban and regional sustainability.

Section: Introductionmentioning

confidence: 99%

Matching Ecosystem Services Supply and Demand through Land Use Optimization: A Study of the Guangdong-Hong Kong-Macao Megacity

et al. 2021

IJERPH

“…Considering the availability of data and the selection of ES indicators, the administrative/management unit is the finest scale for official statistics (Roces-Díaz et al 2017;Lin et al 2018), and a political boundary is suitable for identifying the major drivers of supply and demand changes of ESs in a social ecosystem (Zhang et al 2017b;Cui et al 2019). Selecting a geographical/biophysical unit as the minimal unit is often out of high concern for the heterogeneity of the landscape space, thus enhancing the analysis and interpretation of the quantitative results (Yaneva 2016;Roces-Díaz et al 2017).…”

Section: Different Types Of Minimal Units Showing Different Priority mentioning

confidence: 99%

What are the appropriate mapping units for ecosystem service assessments? A systematic review

Shen

Chen

2021

Ecosyst Health Sustain

Context: Without clear understanding of the units used for ecosystem service (ES) mapping, ES assessment accuracy and the practical application of ES knowledge will be hampered. Method: We systematically reviewed 106 studies over the past 11 years to explore the type, characteristic pattern and deficiencies of mapping units. Result: We proposed that ES mapping units can be categorized into minimal unit for assessing ESs using corresponding indicators and methods, and aggregated unit for analysis and application based on research objectives, and classified the mapping units into five common types. Of the 12 characterizing variables of ES mapping studies, some have been shown to introduce a difference in the selection of mapping units and to exhibit characteristic patterns. We also found that the accuracy of ES assessments based on minimal units was lacking, and aggregated units were insufficient to establish a link between ES knowledge and practice. Conclusion: Herein, we propose possible solutions such as the use of fine spatial resolution grids and the introduction of additional data beyond land cover as supplements to improve the assessment accuracy. To enhance the availability of the results for practice, aggregated units connected with urban planning units should be established at a spatial level suitable for urban management.

“…However, the understanding of ESVs is not comprehensive because multiple types of services interrelate in complex and dynamic ways [13][14][15]. The current research perspectives on ESVs consider these services to be the result of a process: "human-driven factors of ecosystem change !…”

Section: Introductionmentioning

confidence: 99%

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

Liu

et al. 2020

PLoS ONE

The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socioeconomic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.