Satellite view of a river crossing agricultural and forested land

About me

Environmental economist and land-change scientist.

I am a Lecturer at Texas A&M University and hold a PhD in Geography and the Environment from the University of Texas at Austin, completed in 2025. With earlier training in economics, I study land-use change, land markets, agriculture, climate risk, forest restoration, and environmental policy, particularly across the Amazon and Cerrado. My research combines econometrics, GIS, spatial analysis, remote sensing, and land-change modeling to examine how economic incentives and environmental conditions shape decisions by farmers, firms, investors, and public institutions.

My experience connects academic research with policy and applied decision-making. I have evaluated conservation incentives, developed deforestation-risk and opportunity-cost models, studied climate impacts on crop yields, and supported analysis involving sustainable agriculture, carbon markets, climate finance, and nature-based investment. At Texas A&M, I teach in Environmental Systems Science and develop instruction on environmental regulation, policy, quantitative reasoning, and applied environmental analysis. Previously, I taught laboratory sections in spatial data analysis, GIS, remote sensing, and human geography at the University of Texas at Austin.

Publications

See below a selection of publications.

  • Silva, D. S., & Arima, E. (2025). When Land Becomes Capital: Farmland Financialization and Sustainability in Brazil. Environmental Research Letters, 20, 114030.

  • Silva, D., Prolo, C., Braga, P., Witzke, A., Gomes, T., & Velez, L. (2025). Corporate emissions and climate targets: Insights from high-emitting firms in Brazil’s transition to sustainability. PLOS Sustainability and Transformation, 4(9), e0000198.

  • Silva, D. S., & Nunes, S. (2025). Leakage Effects from Reforestation: Estimating the Impact of Agricultural Displacement for Carbon Markets. Land, 14(5), 963.

  • Silva, D. S., Arima, E. Y., dos Reis, T. N., & Rattis, L. (2023). Temperature effect on Brazilian soybean yields, and farmers’ responses . International Journal of Agricultural Sustainability, 21(1), 2173370.

  • Silva, D. S., & Arima, E. Y. (2023). Sustainability Consequences of Making Land Change Decisions Based on Current Climatology in the Brazilian Cerrados. Land, 12(4), 914.

  • Nunes, S., Gastauer, M., Cavalcante, R. B., Ramos, S. J., Caldeira Jr, C. F., Silva, D., ... & Siqueira, J. O. (2020). Challenges and opportunities for large-scale reforestation in the Eastern Amazon using native species. Forest Ecology and management, 466, 118120.

  • Niemeyer, J., Barros, F. S., Silva, D.S., Crouzeilles, R., & Vale, M. M. (2020). Planning forest restoration within private land holdings with conservation co-benefits at the landscape scale. Science of the Total Environment, 717, 135262.

  • Stabile, M. C., Guimarães, A. L., Silva, D.S., Ribeiro, V., Macedo, M. N., Coe, M. T., ... & Alencar, A. (2020). Solving Brazil's land use puzzle: Increasing production and slowing Amazon deforestation. Land use policy, 91, 104362.

  • Latawiec, A. E., Strassburg, B. B., Silva, D.S., Alves-Pinto, H. N., Feltran-Barbieri, R., Castro, A., ... & Beduschi, F. (2017). Improving land management in Brazil: A perspective from producers. Agriculture, Ecosystems & Environment, 240, 276-286.

    • Codes & Data Analysis

    See below some scripts in Python, R, and Stata that you may find useful. Some of them are statistical models that I applied to published papers. I'm making it available as pdf to be easier navigate on this webpage. Just click in the model below to find the pdf.

    Example of R coding to panel regression
    
# load packages and data ####
library("plm")
library("car")

# set data as panel
	db_cerr <- pdata.frame(db_cerr, index=c("geocode","year"))

# panel data analysis at municipal-level data in Cerrado biome ####
# Below the models for pooled, fixed, and random estimators in panel linear model
# Quadractic terms are testing the non-linear effects on yield, e.g. squared temperature
	ols <- plm(log_Y ~Temp_C +I(Temp_C^2) +Precip_mm +I(Precip_mm^2) +as.numeric(year), data=db_cerr, model= "pooling")
	summary(ols) # Results of the pooled panel model 'ols'
	
	fe <- plm(log_Y ~Temp_C +I(Temp_C^2) +Precip_mm +I(Precip_mm^2) +as.numeric(year), data=db_cerr, model= "within")
	summary(fe) # Results of the Fixed effects panel model 'fe'
	
	re <- plm(log_Y ~Temp_C +Precip_mm +as.numeric(year), data=db_cerr, model= "random")
	summary(re) # Results of the Random Effects panel model 're'

# Tests
# Breusch-Pagan (Lagrange Multiplier) test for random effects versus OLS
	plmtest(ols) # if p-value =< 0.05 then reject the H0 and OLS (or try again controlling for AC); H0 is Cov(ai, ui)=0
	
# LM test for fixed effects versus OLS; H0: no FE
	pFtest(fe, ols) # if p-value =< 0.05 then consider H1 (there are fixed effects)
	
# Hausman endogeneity test for fixed versus random effects model
	phtest(fe, re) # if p-value =< 0.05 then RE is inconsistent (reject null hypothesis); the H0 is that individuals RE effects are exogenous

    CV & Contact

    See below my Resume, or download it here.

    Contact or follow me:

    Email: danielsilva@utexas.edu
    University of Texas University of Texas
    LinkedIn LinkedIn
    GitHub GitHub
    ResearchGate ResearchGate
    Google Scholar Google Scholar