Optimizing Carbon Sequestration in Forest Management Plans Using Advanced Algorithms: A Case Study of Greater Khingan Mountains

• Published in: Forests Vol. 14; no. 9; p. 1785
• Main Authors: Zhang, Weitian, Shao, Hanqin, Sun, Haitao, Zhang, Wei, Yan, Qinglun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Algorithms; Analytic hierarchy process; Carbon; Carbon sequestration; Case studies; Climate change; Deforestation; Economic models; Emissions; Emissions control; Environmental aspects; Extreme cold; Extreme weather; Forest fires; Forest management; Forest products; Greenhouse effect; Greenhouse gases; Karush–Kuhn–Tucker algorithm; Logging; Mathematical optimization; Methods; Microorganisms; Mountains; multi-objective optimization; Multiple objective analysis; Optimization models; Paris Agreement; Renewable energy; Simulated annealing; Soil dynamics; Sustainability management; Sustainable forestry; Weather; Wildfires; Wood; China; Mongolia; Heilongjiang China
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f14091785

The Paris Agreement aims to combat climate change by reducing greenhouse gas emissions, with bioenergy identified as a potential solution. However, concerns remain about its impact on carbon stocks and the optimal timing for implementation. To address these challenges, we propose a comprehensive multi-objective optimization model for forest management that maximizes carbon sequestration and economic benefits. Our model integrates three key components: (1) a sophisticated carbon-sequestration model encompassing living plants, wood forest products, and soil and microbial carbon uptake, (2) dynamic factors such as forest fires and extreme weather events, and (3) an economic benefits model focused on wood-processing products. We optimized the forest-management strategy over ten years by leveraging the simulated annealing and Karush–Kuhn–Tucker (KKT) algorithms. Through simulations using data from China’s Greater Khingan Mountains region, we explored the optimal logging plans for maximizing carbon sequestration without external factors. Our results revealed that the optimized logging plans significantly enhance carbon sequestration compared to proportionally averaged logging plans. Next, we investigated the impact of external factors on forest management, specifically wildfires and extreme weather events. Our findings demonstrate that wildfires have a more-substantial detrimental effect on the absolute value of carbon sequestration and the extent of improvement achieved through model optimization. At the same time, extreme cold primarily affects the growth rate of carbon sequestration. We employed a linear-weighting approach and the Analytic Hierarchy Process (AHP) to address the trade-offs between carbon sequestration and economic benefits to transform the multi-objective optimization function into a single objective. The results showed that the optimized harvesting schedule can lead to improved economic benefits compared to uniformly harvesting trees. Moreover, the joint optimization approach enabled us to identify optimal solutions that balance carbon sequestration and economic benefits, offering sustainable forest management strategies. Our study provides valuable quantitative insights into forest management strategies that balance carbon sequestration and economic benefits, making it highly relevant for real-world applications.