Key Takeaways & Executive Findings
- •• • Constructed wetlands achieve the lowest comprehensive cost at ¥1.01 yuan/m³ and exhibit the lowest environmental load across most impact categories, with only slightly higher land resource consumption intensity, making them the preferred environmental-economic option for non-point source pollution control. • • Ecological intercepting ditches show moderate comprehensive cost (¥1.57 yuan/m³) but higher impacts in metal resource consumption (54.60% contribution from rebar) and other categories due to rebar and base fertilizer inputs, indicating a need to optimize material selection. • • Vegetation restoration projects have the highest comprehensive cost (¥61.14 yuan/m³) and the most significant environmental impacts, with human carcinogenic toxicity elevated, primarily driven by concrete use (73.73% contribution) and base fertilizer, highlighting the environmental burden of concrete-intensive engineering. • • Life cycle environmental costs dominate total costs across all three measures, accounting for 77.83% (constructed wetlands), 92.80% (ecological intercepting ditches), and 90.98% (vegetation restoration) of comprehensive costs, underscoring the necessity of integrating full life cycle environmental impacts into decision-making for watershed restoration projects.
Abstract
To identify optimal watershed remediation pathways under environmental and economic dimensions, an integrated environmental-economic impact assessment framework combining life cycle assessment (LCA) and life cycle costing (LCC) based on openLCA was established, using 1 m³ of treated wastewater as the functional unit. This framework comprehensively evaluated the environmental impacts and economic costs of three ecological buffer measures—constructed wetlands, ecological intercepting ditches, and vegetation restoration projects—in non-point source pollution control. The results indicated that constructed wetlands offer the optimal environmental-economic profile, featuring the lowest comprehensive cost (¥1.01 yuan/m³) and the lowest load across most environmental impact categories, with only slightly higher land resource consumption intensity. Ecological intercepting ditches exhibited higher impacts in areas such as metal resource consumption due to the use of rebars and base fertilizer inputs, resulting in a moderate comprehensive cost (¥1.57 yuan/m³). Vegetation restoration projects incurred the highest comprehensive cost (¥61.14 yuan/m³) and produced the most significant environmental impacts, with elevated indicators such as human carcinogenic toxicity. This primarily stemmed from the extensive use of concrete grass pavers and base fertilizer in rural river sections. These findings provide quantitative references for environmental-economic integrated evaluation and decision-making regarding ecological buffer zone engineering schemes in similar watersheds.
1. Introduction
Non-point source pollution has become a critical bottleneck in watershed water environmental management, particularly as point source pollution is increasingly controlled. The complexity of non-point source pollution, with its diverse origins and uneven spatial distribution, makes its remediation significantly more challenging than point source pollution. Ecological buffer zones, as key engineering measures along riverbanks, serve as barriers to intercept pollutants, restore vegetation, and reduce pollution loads entering rivers, while also providing ecosystem services such as carbon sequestration and soil conservation. However, the selection of appropriate buffer zone measures has traditionally been based on pollutant removal efficiency alone, often neglecting the broader environmental and economic burdens across the entire life cycle of these engineered systems.
Existing commercial approaches for ecological buffer zone implementation lack a systematic framework to quantify the trade-offs between environmental impacts and economic costs. This study addresses this gap by establishing an integrated LCA-LCC framework using openLCA, with a functional unit of 1 m³ of treated wastewater. The framework is applied to compare three typical measures: constructed wetlands, ecological intercepting ditches, and vegetation restoration projects. By quantifying the environmental impacts across 18 categories and life cycle costs, this research provides a quantitative basis for selecting optimal remediation pathways that balance environmental performance and economic feasibility, thereby supporting sustainable decision-making in watershed management.
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SHI Wanxian, XIONG Lijun, GUO Fei, LEI Jingcheng, XU Kangning (2026). Environmental Impact and Cost Analysis of Ecological Buffer Zones from an LCA-LCC Perspective. Journal of Environmental Engineering Technology. https://doi.org/10.13205/j.hjgc.202606004
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Frequently Asked Questions
What are the dominant environmental impact drivers for each ecological buffer measure, and how do they influence the overall environmental profile?
For constructed wetlands, the main environmental impact sources are base fertilizer application (40.17%) and earthwork (25.43%). For ecological intercepting ditches, rebar contributes 54.60% of the environmental impact, primarily affecting metal resource consumption. For vegetation restoration projects, concrete use dominates, accounting for 73.73% of the environmental impact, leading to elevated human carcinogenic toxicity and other indicators. These drivers dictate the environmental hotspots and guide material substitution strategies.
How do the life cycle costs of the three measures compare, and what is the proportion of environmental costs in the total comprehensive cost?
The comprehensive costs per cubic meter of wastewater treated are ¥1.01 for constructed wetlands, ¥1.57 for ecological intercepting ditches, and ¥61.14 for vegetation restoration projects. Environmental costs dominate the total life cycle costs, comprising 77.83% for constructed wetlands, 92.80% for ecological intercepting ditches, and 90.98% for vegetation restoration projects. This indicates that ignoring environmental externalities would severely underestimate the true cost of these engineering measures.
What is the uncertainty associated with the environmental impact results, and how reliable are the comparisons?
Uncertainty analysis, as presented in Table 5, shows coefficient of variation (CV) values for most impact categories below 5% for all three measures, indicating high reliability. For example, CVs for climate change are 3.17% for constructed wetlands, 2.72% for ecological intercepting ditches, and 4.05% for vegetation restoration. Some categories like human carcinogenic toxicity for constructed wetlands have a higher CV of 11.28%, but overall the results are robust enough for comparative conclusions.
Which environmental impact categories show the most significant differences among the three measures, and what are the implications for technology selection?
Vegetation restoration projects exhibit significantly higher impacts in categories such as human carcinogenic toxicity, terrestrial ecotoxicity, and fossil resource scarcity compared to the other two measures. For instance, the standard deviation for human carcinogenic toxicity is 4.01E-04 for vegetation restoration versus 4.67E-06 for constructed wetlands. This suggests that vegetation restoration, despite its ecological benefits, carries a high environmental burden due to concrete-intensive construction, making it less favorable from a life cycle perspective unless alternative materials are used.
How does the functional unit of 1 m³ of treated wastewater affect the comparability of the results across different scales?
Using 1 m³ of treated wastewater as the functional unit allows for a normalized comparison of environmental impacts and costs per unit of treatment, facilitating scalability. However, the results are specific to the system boundaries and assumptions of this study. For larger-scale applications, the relative performance may change due to economies of scale or variations in material transportation distances. Nevertheless, the framework provides a consistent basis for comparing different measures at a given scale.
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