TANG, Juk-Sen (唐煜燊)

McGill University

Faculty of Agricultural and Environmental Sciences

Montreal, QC, Canada

I am an undergraduate student at McGill University, pursuing a B.Sc. in Agricultural Economics within the Faculty of Agricultural and Environmental Sciences, with a minor in Computer Science. My research interests lie at the intersection of agricultural economics, environmental and resource economics, and applied econometrics.

I am currently a Research Assistant at the Key Lab of Urban Agriculture (North China), Ministry of Agriculture and Rural Affairs / Beijing Academy of Agricultural and Forestry Sciences, where I contribute to research on agricultural productivity and policy evaluation. Previously, I studied at Peking University’s Guanghua School of Management as a visiting student. I also serve as Secretary of the Chinese Students & Scholars Association (CSSA) at McGill University.

My recent research focuses on:

Agricultural total factor productivity (TFP) and spatial spillover effects (published in Agriculture)
Carbon pricing policy certainty and green innovation
Causal machine learning for governance and policy evaluation
Urban scaling laws and regional economic coordination
Machine learning evaluation methodology in food safety

Agriculture
Agricultural Productivity and Its Spatial Spillover Effects in China

Juk-Sen Tang, Hongwei Lu, Tianyi Gong, and Junhong Chen

Agriculture, Feb 2026

Abs DOI Bib HTML

Estimated provincial agricultural TFP across 31 Chinese provinces (2014–2023) using a novel IV-LP-ACF-SAR framework; identified mechanization as the dominant output driver and significant negative spatial spillovers in scale efficiency.
@article{tang2026agricultural, title = {Agricultural Productivity and Its Spatial Spillover Effects in China}, author = {Tang, Juk-Sen and Lu, Hongwei and Gong, Tianyi and Chen, Junhong}, journal = {Agriculture}, year = {2026}, month = feb, volume = {16}, number = {5}, pages = {543}, doi = {10.3390/agriculture16050543}, }
Preprint
Causal Machine Learning Reveals Divergent Effectiveness of Governance Modes for Sustainable Agricultural Transformation

Juk-Sen Tang, Haobo Zhang, Lily Shan, and Junhong Chen

Preprint, Mar 2026

Abs DOI Bib HTML

Applied Causal Forest with DML to three policy reforms across 2,700+ Chinese counties; showed that governance design, not fiscal magnitude, determines effectiveness, and data-driven targeting outperforms administrative allocation.
@article{tang2026causal, title = {Causal Machine Learning Reveals Divergent Effectiveness of Governance Modes for Sustainable Agricultural Transformation}, author = {Tang, Juk-Sen and Zhang, Haobo and Shan, Lily and Chen, Junhong}, journal = {Preprint}, year = {2026}, month = mar, doi = {10.20944/preprints202603.0372.v1}, }
Preprint
Scaling Laws, Poverty Traps, and the Urbanization Crossover in Chinese County Economies

Juk-Sen Tang

Preprint, Mar 2026

Abs DOI Bib HTML

Estimated sector-specific urban scaling exponents for approximately 2,800 Chinese counties (2000–2023); identified an urbanization crossover threshold and persistent scaling deficits in 832 designated poverty counties.
@article{tang2026scaling, title = {Scaling Laws, Poverty Traps, and the Urbanization Crossover in Chinese County Economies}, author = {Tang, Juk-Sen}, journal = {Preprint}, year = {2026}, month = mar, doi = {10.20944/preprints202603.0443.v1}, }
Preprint
Are Food Safety Classifiers Learning Hazards or Memorizing Firms? Entity-Level Leakage in FDA Recall Severity Prediction

Peilun Li and Juk-Sen Tang

Preprint, Mar 2026

Abs DOI Bib HTML

Benchmarked ML classifiers on 28,448 FDA recall records; exposed that 92% of apparent performance stems from firm-level memorization and proposed group-aware evaluation protocols.
@article{li2026foodsafety, title = {Are Food Safety Classifiers Learning Hazards or Memorizing Firms? Entity-Level Leakage in FDA Recall Severity Prediction}, author = {Li, Peilun and Tang, Juk-Sen}, journal = {Preprint}, year = {2026}, month = mar, doi = {10.20944/preprints202603.0343.v1}, }
Preprint
Carbon Price Certainty and Green Innovation: Evidence from Canada’s Federal Backstop Policy

Juk-Sen Tang

Preprint, Nov 2025

Abs DOI Bib HTML

Exploited Alberta’s 2019 backstop adoption as a natural experiment across 1,381 firms; found that legislative clarification, not announcement, drives green patenting, while output expansion fully offsets emission intensity gains.
@article{tang2025carbon, title = {Carbon Price Certainty and Green Innovation: Evidence from Canada's Federal Backstop Policy}, author = {Tang, Juk-Sen}, journal = {Preprint}, year = {2025}, month = nov, doi = {10.20944/preprints202511.1753.v1}, }

TANG, Juk-Sen (唐煜燊)

news

latest posts

selected publications