Prof. Robert Ugochukwu Onyeneke (p), Dr Richmond Atta-Ankomah, Dr Richard Nkrumah, Dr Richard Nkrumah, Mrs Chinenye Onyeneke, Dr Fidelis Nwosu

Economies in Africa are often characterized as natural-resource dependent and most susceptible to climate change; they also face drastic depletion of their biocapacity. Climate change and biocapacity depletion jointly affects the level of value added to agriculture, fishing and forestry. However, studies documenting the impact of climate change and biocapacity on agriculture value added are rare. In this study, we assessed the impact of climate change and biocapacity on agriculture fishing and forestry value-added in 38 African countries over 15-years span. We applied several estimation techniques including the pooled group mean and dynamic fixed effects panel autoregressive distributed lag (PARDL) simulations, with data from 2006 to 2020. Results show that precipitation, temperature, agricultural employment, and gross fixed capital formation significantly increase value added in the long and short term, whereas biocapacity exhibits a negative relationship, implying unsustainable use of natural capital. The result indicates stronger long-term impacts of rainfall and temperature since production could be well planned with better weather predictability in the long run. The findings highlight the intricate balance between economic growth and ecological limits in Africa’s primary sectors. By integrating biocapacity into the climate–growth nexus, the study provides novel empirical evidence that strengthens the ecological economics discourse and offers practical guidance for promoting climate-resilient and environmentally sustainable agricultural development in Africa.

Dr. Poema Isis Souza (p), Dr Rosa Duarte

Empirical research on the Environmental Kuznets Curve (EKC), which points at a systematic relationship between economic activity and environmental degradation, often characterized by an inverted U shape, still leaves important gaps, particularly regarding spatial interdependence and within-country heterogeneity. In this conxtex, the main objective of this paper is to find evidence of the existence and evolution of an ECK relartionship and its spatial effects for the regions of the European Union, (NUTS 2), assessing the extent to which emissions display spatial spillovers across neighboring territories in the period 2014-2023.The level of disaggregation of the NUTS 2 region allows for a more efficient capture of regional characteristics compared to models that use data for countries. By incorporating spatial effects into the estimated econometric models, it is possible to observe the neighborhood effects of pollution and the socioeconomic characteristics of the regions. Thus, it is important to add to the econometric models of the EKC the effects of the spread of environmental impacts in areas close to the polluting region using spatial econometrics to try to capture the spillovers of environmental damage by including spatial dependencies and relaxing the assumption of homogeneity of the EKC. So, the methodology used was panel data with spatial effects. The dependent variable is greenhouse gas emissions measured in CO₂ equivalents. Explanatory variables include, among others, the usual GDP per capita and population, but also considers the role of agriculture, industry, and construction, as well as the regional orientation to high-technological sectors. The results support the presence of an inverted U-shaped EKC and reveal statistically significant spatial effects, indicating a positive relationship between the level of GHG emissions in neighboring regions and a positive spatial effect of technology on pollution reduction in these areas. Some policy-relevant extensions of the model have been also explored. For instance, the study of policy and energy variables such as exposure to regional and national environmental regulation, renewable penetration, energy intensity, and the regional electricity mix could help to identify specific policy drivers of decarbonisation rather than only relying on income correlations. Moreover, introducing heterogeneity through regional “clubs” will test whether the EKC shape and the turning point differ across industrial, rural, and highly urbanised regions, supporting differentiated, place-based policies.

Dr. André Luis S Chagas (p), Dr. Sandy Dall'Erba

Climate change is intensifying extreme weather events globally, with Brazil experiencing its worst drought since 1950. Nearly 60\% of its territory is affected, creating major challenges for agriculture and trade. This paper examines the impact of past droughts on Brazil’s wheat production and global imports. While wheat is less dominant than soybeans or corn, it is essential for food security, hence it is largely imported and increasingly cultivated in Paraná, Rio Grande do Sul, and Santa Catarina. Yet, it remains understudied compared to the other two crops. The research employs the innovative endogenous network difference-in-differences (DID) method developed by the PIs, which captures both direct and indirect drought effects on trade networks. Unlike conventional DID approaches, this method accounts for spillover impacts on trade partners, providing deeper insights into global trade dynamics and adaptation. In line with the IPCC’s call for research on climate-driven agricultural changes, the paper fosters international collaboration between Prof. Dall’erba and Prof. Chagas and supports interdisciplinary graduate training. Findings will inform adaptation policies, strengthen food security, and highlight the need for global cooperation in addressing climate impacts. All data and codes will be openly shared to ensure transparency and reproducibility, contributing to sustainable agricultural solutions worldwide.

Prof. Fernando Perobelli (p), Prof. Eduardo Haddad, Sr Gabriel Arcanjo, Sr Marcio Henriques

Brazil is among the world’s largest producers and exporters of agricultural commodities—such as soybeans, corn, cotton, coffee, sugar, orange juice, beef, and poultry—as well as forest products, especially pulp. These activities are key drivers of economic and social development across multiple regions, and the sectors of Agriculture, Livestock, Forestry Production, Fishing, and Aquaculture are central to the Brazilian economy. They are estimated to contribute directly about 7% of Brazil’s Gross Domestic Product (GDP) and to account for approximately R$72 billion in exports (IBGE, 2024; Brazil, MDIC, 2024). Despite their economic relevance, these sectors are also responsible for a substantial share of Brazil’s greenhouse gas (GHG) emissions, largely due to deforestation, land conversion, and burning of native vegetation. In recent years, agricultural activities have accounted for around 30% of national GHG emissions, while land use, land-use change, and forestry (LULUCF) have represented roughly 40% of national emissions—much of which is associated with agriculture-related activities (SIRENE/MCTI, 2025).
Brazil’s manufacturing industry also plays a pivotal role in the national economy, representing about 15.3% of GDP (CNI, 2024a) and contributing 46% of Brazil’s exports of goods and services. It is a major employer, accounting for approximately 14.7% of formal jobs (CNI, 2024a), and is therefore critical both for job creation and for sustaining long-term economic growth. The diversity and complexity of Brazilian manufacturing strengthen Brazil’s relevance in global supply chains. Activities classified under CNAE Section C have traditionally relied heavily on fossil fuels, implying substantial decarbonization potential. This potential can be realized through fuel switching toward renewable energy sources—such as bioenergy and biofuels—as well as through the adoption of new technologies and the electrification of production processes.
Drawing on the Green Taxonomy framework, which classifies Brazilian productive sectors according to climate vulnerability and adaptive capacity, this study combines the sectoral relevance of Brazil’s economy with an input–output matrix calibrated to 2019 and emissions-intensity vectors for 68 productive sectors. This approach highlights the interdependence between high- and low-emitting sectors and enables an assessment—through intersectoral linkages—of the direct and indirect impacts, as well as external effects, associated with sectoral emissions in the Brazilian economy.