Sustainability strategies for carbon sequestration in South Tangerang City using InVEST model to support sustainable urban planning

InVESTモデルを用いた南タンゲラン市の炭素隔離のための持続可能性戦略：持続可能な都市計画を支援する (AI 翻訳)

Marianus Irfan Takang Peri, Yudi Chadirin, Wonny Ahmad Ridwan

Springer Link (Chiba Institute of Technology)📚 査読済 / ジャーナル2026-04-23#炭素会計

DOI: 10.1051/bioconf/202623402011/pdf

原典: https://doi.org/10.1051/bioconf/202623402011/pdf

🤖 gxceed AI 要約

日本語

南タンゲラン市の急速な都市化による炭素貯留量の減少を、リモートセンシングとInVESTモデルで分析。2016～2025年で炭素貯留量は9.6%減少し、森林が総炭素の67.1%を占める。都市開発圧力とNDVIの相関が強く、植生保全政策の重要性を示す。

English

This study examines carbon stock changes in South Tangerang City from 2016-2025 using remote sensing, CA-Markov, and InVEST models. Carbon stocks declined by 9.6% (90,028 tC), with forests holding 67.1% of total carbon. Strong correlations between forest area and carbon stocks highlight the need for vegetation conservation in rapidly urbanizing areas.

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

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本でもSSBJや有報における自然資本・カーボンアカウンティングの議論が進む中、本手法は都市レベルでの炭素貯留評価に応用可能。ただしインドネシアの事例であり、日本の都市への直接適用には調整が必要。

In the global GX context

While global disclosure frameworks like ISSB and CSRD focus on corporate carbon accounting, this study provides a spatially explicit method for urban ecosystem carbon assessment, relevant for city-level climate action plans and nature-based solutions.

👥 読者別の含意

🔬研究者:The InVEST-CA-Markov methodology for spatiotemporal carbon stock estimation can be replicated in other urban contexts.

🏢実務担当者:Urban planners can use the identified predictors (patch size, NDVI) to prioritize green space conservation.

🏛政策担当者:Evidence that forest area strongly correlates with carbon stocks supports urban vegetation preservation policies.

📄 Abstract（原文）

Rapid urbanisation in South Tangerang City has triggered substantial conversion of green spaces, critically impacting the carbon-sequestration capacity of urban ecosystems. This study integrates remote sensing technology, Cellular Automata-Markov predictive modelling, and the InVEST Carbon Storage model to examine the spatiotemporal dynamics of land cover and estimate carbon stocks over the 2016–2025 period. Random Forest classification achieved an accuracy of 93.61% with a kappa coefficient of 0.874, demonstrating robust temporal consistency across all observation periods. The analysis revealed a decline in carbon stocks of 90,028 tC (9.6%), from 939,287 tC to 849,259 tC, with carbon density decreasing from 64.93 to 58.04 tC/ha. Forest ecosystems accounted for 67.1% of total carbon stocks, despite absolute losses of 106,336 tC. Landscape metrics demonstrated structural improvements, with mean patch size increasing by 50.4% and connectivity enhancing by 14.7%, indicating successful habitat consolidation. Key predictors of carbon sequestration sustainability include mean patch size as the strongest predictor of carbon density (r = 0.82), urban development pressure (r = 0.847), and NDVI-carbon correlation (r = 0.85). A strong positive correlation (r2 = 0.87) between forest area and carbon stocks confirms that vegetation conservation policies are essential for maintaining urban carbon sequestration capacity in rapidly urbanising regions.

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.1051/bioconf/202623402011/pdffirst seen 2026-05-17 04:44:34

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