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Record W4408429798 · doi:10.5194/egusphere-egu25-14971

Challenges and enablers of co-achieving ambitious global climate and biodiversity targets

2025· preprint· en· W4408429798 on OpenAlexaboutno aff
Yazhen Wu, Stefan Frank, Kazuaki Tsuchiya, David Leclère, Oliver Fricko, Shinichiro Fujimori, Mykola Gusti, Tomoko Hasegawa, Andrey Lessa Derci Augustynczik, Tamás Krisztin, Sibylle Rouet-Pollakis, Michael Wögerer, Hans van Meijl, Willem‐Jan van Zeist, Akiko Hirata, Volker Krey, Haruka Ohashi, Kiyoshi Takahashi, Keywan Riahi, Peter Havlík

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityBusinessEnvironmental resource managementEnvironmental planningClimate changeEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Background: The need to address the dual challenges of climate change and biodiversity loss is pressing and requires collective efforts in both land-use and energy sectors. However, the interactive impacts between mitigation and biodiversity conservation measures, especially the indirect impacts through the energy-land nexus, have not been comprehensively investigated. The question arises as to whether and what levels of synergies or trade-offs exist between mitigation and biodiversity targets, and what are the implications on energy system decarbonization pathways and corresponding mitigation costs.Methodology: By applying and comparing two modelling frameworks that link integrated assessment models (AIM, MESSAGEix-GLOBIOM) and biodiversity models (Figure 1), we explore the system-wide synergies and trade-offs between the ambitious climate and biodiversity targets included in the Paris Agreement and Kunming-Montreal Global Biodiversity Framework (KMGBF). Four forward-looking policy scenarios with different mitigation and biodiversity conservation ambitions are simulated for the period 2010-2070 to quantify the land-use dynamics, greenhouse gas emissions, biodiversity indicators, as well as energy transformation pathways under different policy targets. Additional sensitivity analysis and decomposition analysis allow us to explore the implications of alternative mitigation pathways on the key findings, and to disentangle the effects of individual policy measures within the mitigation and biodiversity portfolios.Results: Scenario results show that despite biodiversity synergies from stringent mitigation measures for the 1.5°C target, area-based biodiversity conservation measures are not enough to revert the declining trends of biodiversity. Biodiversity losses can be halted or decelerated with combined mitigation and biodiversity efforts, but until 2070 global biodiversity cannot restore its 2010 levels. On the other hand, due to the energy-land nexus, deploying biodiversity conservation measures can double the carbon price in line with the 1.5°C target and increase global gross domestic product loss by 0.7% by 2070. However, the availability of alternative negative emission technologies and increased pasture production efficiency can act as enablers to reduce the additional costs to achieve 1.5°C-mitigation induced by the biodiversity target. Besides, the large land demand for co-achieving stringent mitigation and biodiversity targets can increase the global average price of agricultural products by 35-53% in 2070 and reduce food consumption. Avoiding the potential negative implications on food security would entail substantial food system transformation efforts. Our results indicate that the challenges of co-achieving the 1.5°C and KMGBF targets can be amplified via cross-sectoral impacts on the energy system and be greater than previously thought. This calls for more careful policy design to simultaneously address the two targets while limiting the trade-offs with food security or the economic feasibility of decarbonization.Figure 1. Overview of research design

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0060.006
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.273
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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