森林観光のカーボンフットプリント算定と持続可能なサプライチェーン管理における削減戦略の最適化

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:動的炭素会計と多目的最適化の統合手法に関心のある研究者に、観光業での実証例を提供する。

🏢実務担当者:観光事業者やサプライチェーン管理者が、ISO 14067に基づく算定と削減戦略の立案に活用できる。

🏛政策担当者:地域観光政策において、炭素削減と経済・社会便益のバランスを取るための参考になる。

This study aims to construct a high-precision and dynamic carbon footprint accounting model and provide feasible emission reduction strategy combinations to address specific issues in the forest tourism supply chain, such as blurred boundaries in carbon footprint accounting, static lag in emission factors, and divergent emission reduction strategies in balancing environmental, economic, and social benefits. Firstly, this paper determines the lifecycle accounting boundaries based on the ISO 14067 (International Organization for Standardization) standard, covering upstream material supply, midstream scenic area operation, downstream waste disposal, and tourist return. Then, this paper collects and preprocesses multi-source heterogeneous data, including smart meter data and GPS (Global Positioning System) trajectories, to address data silos and noise issues. Next, this paper introduces a triple dynamic correction mechanism based on load factors, grid time series marginal emission factors, and biodynamic parameters to achieve spatiotemporal dynamic perception of carbon footprint. Also, this paper takes the dynamic accounting results as input and constructs a multi-objective optimization model with clean energy substitution rate as the decision variable. Finally, this paper uses NSGA-II (Non dominated Sorting Genetic Algorithm II) and elite strategy to solve the Pareto front, achieving closed-loop collaboration between accounting accuracy and multi-dimensional objective trading operations. The experimental results show that the average absolute percentage error of the carbon footprint time series prediction method in this paper is 2.01%; the unit emission reduction cost is 40.97 yuan/ton of carbon dioxide equivalent; the tourist satisfaction index reaches 0.87. The method proposed in this paper combines triple dynamic factor correction with three-dimensional NSGA-II multi-objective optimization, significantly improving the accuracy of carbon footprint calculation in the forestry tourism supply chain, and formulating emission reduction strategies that consider environmental, economic, and social benefits.

• openalex https://doi.org/10.3389/frsus.2026.1927146first seen 2026-11-05 05:20:38 · last seen 2026-11-06 04:59:56