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Record W4415610513 · doi:10.54693/piche.05315

China's strategic response to climate change: policies, challenges, and pathways to carbon neutrality

2025· article· W4415610513 on OpenAlexaff
Zaeem Bin Babar, Adal Farooq, Ubaid Ur Rehman Zia, Hammad Hassan, Hassan Zeb, Muhammad Sarfraz Akram, Abdul Rauf, Muhammad Ansar, Fawad Ashraf

Bibliographic record

VenueJournal of the Pakistan Institute of Chemical Engineers · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGreenhouse gasCarbon neutralityNeutralityClimate changeClimate policyEnergy policyChinaCarbon priceSustainable development

Abstract

fetched live from OpenAlex

Climate change, driven by greenhouse gas (GHG) emissions, poses a significant threat to China's environment, economy, and society. This review paper examines China's evolving response to this challenge. It explores China's international commitments, including the Paris Agreement and its Nationally Determined Contributions (NDCs) aiming for carbon neutrality by 2060. Additionally, the paper analyzes domestic policies like the 1+N Climate Policy framework, the 14th Five-Year Plan (FYP) for Energy, and the National Emissions Trading Scheme. These policies highlight China's multi-pronged approach, focusing on GHG reduction and a comprehensive energy sector transformation. The paper emphasizes the importance of evaluating these policies for effectiveness and acknowledges the challenges China faces, such as balancing climate goals with economic growth. Ultimately, the paper argues that China's strategic approach signifies a growing recognition of the need for climate action. The success of this approach, coupled with continued advancements in clean energy technologies and international collaboration, will be paramount in achieving carbon neutrality and securing a sustainable future.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.281
Teacher spread0.207 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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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