School of Architecture, Planning, and Environmental Policy, University College Dublin, Richview, Clonskeagh, Dublin, Ireland.
The Frederick S. Pardee Center for the Study of the Longer Range Future, Boston University, Boston, MA 02215, USA.
Sci Total Environ. 2020 May 1;715:137004. doi: 10.1016/j.scitotenv.2020.137004. Epub 2020 Feb 7.
Most of the Earth's Ecosystem Services (ESs) have experienced a decreasing trend in the last few decades, primarily due to increasing human dominance in the natural environment. Identification and categorization of factors that affect the provision of ESs from global to local scales are challenging. This study makes an effort to identify the key driving factors and examine their effects on different ESs in the Sundarbans region, India. We carry out the analysis following five successive steps: (1) quantifying biophysical and economic values of ESs using three valuation approaches; (2) identifying six major driving forces on ESs; (3) categorizing principal data components with dimensionality reduction; (4) constructing multivariate regression models with variance partitioning; (5) implementing six spatial regression models to examine the causal effects of natural and anthropogenic forcings on ESs. Results show that climatic factors, biophysical factors, and environmental stressors significantly affect the ESs. Among the six driving factors, climate factors are highly associated with the ESs variation and explain the maximum model variances (R = 0.75-0.81). Socioeconomic (R = 0.44-0.66) and development (R = 27-0.44) factors have weak to moderate effects on the ESs. Furthermore, the joint effects of the driving factors are much higher than their individual effects. Among the six spatial regression models, Geographical Weighted Regression (GWR) performs the most accurately and explains the maximum model variances. The proposed hybrid valuation method aggregates biophysical and economic estimates of ESs and addresses methodological biases existing in the valuation process. The presented framework can be generalized and applied to other ecosystems at different scales. The outcome of this study could be a reference for decision-makers, planners, land administrators in formulating a suitable action plan and adopting relevant management practices to improve the overall socio-ecological status of the region.
在过去几十年中，大多数地球生态系统服务（ESs）呈下降趋势，主要是由于人类对自然环境的主导地位不断增强。从全球到地方尺度，识别和分类影响 ESs 提供的因素具有挑战性。本研究努力确定关键驱动因素，并研究它们对印度孙德尔本斯地区不同 ESs 的影响。我们通过以下五个连续步骤进行分析：（1）使用三种评估方法量化 ESs 的生物物理和经济价值；（2）确定对 ESs 的六个主要驱动力；（3）通过降维对主要数据组件进行分类；（4）构建多元回归模型并进行方差分解；（5）实施六个空间回归模型以检验自然和人为因素对 ESs 的因果影响。结果表明，气候因素、生物物理因素和环境胁迫因素对 ESs 有显著影响。在六个驱动因素中，气候因素与 ESs 的变化高度相关，解释了最大的模型方差（R=0.75-0.81）。社会经济（R=0.44-0.66）和发展（R=27-0.44）因素对 ESs 的影响较弱。此外，驱动因素的联合效应远高于其单独效应。在六个空间回归模型中，地理加权回归（GWR）表现最准确，解释了最大的模型方差。所提出的混合评估方法综合了 ESs 的生物物理和经济估计，并解决了评估过程中存在的方法偏差。所提出的框架可以推广并应用于不同尺度的其他生态系统。本研究的结果可以为决策者、规划者和土地管理者提供参考，以便制定合适的行动计划并采取相关的管理实践，以提高该地区的整体社会生态状况。
