MétaCan
Menu
← Back to cohort
Record W3133045156 · doi:10.18174/531735

New rice biotechnology in China: approval, adoption, and stakeholders’ views

2021· dissertation· en· W3133045156 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
Fundersnot available
KeywordsChinaBiotechnologyAgricultural biotechnologyBusinessPolitical scienceBiologyAgricultureLaw

Abstract

fetched live from OpenAlex

This study examines the approval process for GM crops and stakeholders’ views on them, and it analyzes the economic impacts of adopting new rice biotechnology in China. The applied approaches can be adapted and extended to other products with similar concepts and backgrounds.The main body of the study consists of four chapters (Chapters 2–5). In Chapter 2, I examine stakeholder participation in the online public debate on GMOs in China and how the debate influences the reactions of stakeholders over time. I analyze posts on Weibo, a Chinese microblog website, using discourse network analysis to identify coalitions and communities within each coalition. The findings reveal a strong opposition to GM crops and the existence of two competing coalitions of supporters and opponents. The number of supporting posts by anonymous individuals has risen in recent years, and the positions of stakeholders have changed over time.The general GMO environment, as discussed in Chapter 2, is important for understanding the current GMO regulatory system in China. In Chapter 3, I review the complex Chinese regulatory system involving various departments and a host of regulatory documents for approving GM crops. I analyze the trend of the approval process of imported GM crops in China as well as the factors affecting their approval to better understand the complex underlying process. The results show that the average time to obtain approval for imported GM crops in China increased by around 16 months after ca. 2010 due to increased public concerns about GM crops. Worldwide, China approves GM crops, on average, around one year earlier than the European Union but lags, on average, 4.2 years behind the United States and 4.9 years behind Canada.Like many other countries, China experiences regulatory delay, which Chapter 3 analyzes. This has economic consequences. In Chapter 4, I determine the opportunity cost of postponing Bt rice commercialization in China between 2009 and 2019 to be 12 billion US dollars per year considering the external costs of pesticide to be 1.8 million US dollars per year. The positive impacts of technology spill-over, the maximum adoption rate, and the diffusion rate on the cost of postponement are analyzed. The results show that the continuous postponement of Bt rice introduction in China has come at a substantial economic cost that includes not only the direct economic losses of efficiency at higher prices of rice for consumers but also human health and environmental costs.The current blockage of cultivating GM crops resulting from public debate, together with the complex regulatory process in China, have implications for the potential regulation of crops derived by genome editing. In Chapter 5, I economically assess the market potential of CRISPR rice considering the uncertainty of insect pest severity and provide a framework to assess the influence of its factors ex ante. CRISPR rice has the trait of insect resistance, like GM rice, but like rice developed by traditional plant-breeding techniques, no new gene was inserted. I develop a microeconomic model of a representative rice farmer who allocates her land into conventional and CRISPR rice under the uncertainty of insect pest severity considering the yields differential. The results show that the representative farmer benefits from cultivating both types of rice by 1.84 billion US dollars annually. Based on profits per hectare, I expect the farmer to favor CRISPR rice because it can bring almost 20% more profit per unit of land in a weak pest scenario and nearly seven times more in a severe pest scenario. Monte Carlo simulations show three main groups of factors that affect the optimal planting share of CRISPR rice in China: the regulatory environment, the market situation, and the state of the technology. The ex ante economic assessment of CRISPR rice contributes to the current heated discussion on how to regulate genome editing in China.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.229
Teacher spread0.202 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicBiofuel production and bioconversion→French-language works237,207→