Tag: Q42

MEASURING GROSS EMPLOYMENT GENERATION POSSIBILITIES IN THE BIOGAS VALUE CHAIN IN SOUTHERN BRAZIL

Gustavo FERRO

Associate Professor and Independent Researcher, Universidad del CEMA (UCEMA) and CONICET. gaf97@ucema.edu.ar

gferro05@yahoo.com.ar.

M. Priscila RAMOS 

Adjunct Professor and Adjunct Researcher, Universidad de Buenos Aires. Facultad de Ciencias Económicas. CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política de Buenos Aires.

mpramos@economicas.uba.ar

Carlos A. ROMERO

Adjunct Professor and Researcher at CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política (IIEP-BAIRES).

cromero@economicas.uba.ar

Abstract

Biogas is generated from substrates derived from agriculture and cattle, agroindustry (slaughterhouses, flour, and sugar mills), urban solid waste, and sewerage treatment. This study measures the current and potential production and gross employment in the biogas value chain in three southern states in Brazil (Paraná, Santa Catarina, and Rio Grande do Sul). We offer two contributions: first, an input-output methodology to focus on the problem of disparate or nonexistent sectoral information, both in monetary and physical units; second, the quantitative results of output and gross job creation derived from shocks at the regional level. We calibrate input-output matrices of the three states with compatibilized sector entries, opening new ones for those not included in official statistics (derived from specific surveys). Once the baseline has been established, we consider three scenarios: demand-pull that achieves full capacity utilization, supply push that addresses new investments in the sector assuming guaranteed demand, and full utilization of substrates supply for biogas production. Employment multipliers are in line with literature on comparative activities found elsewhere in the world. Our findings support the hypothesis of the relatively high labor intensity in the biogas industry.

Keywords: biogas, Brazil, input-output, employment

JEL classification: Q42, R15

 pp. 21-37

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ENERGY CROPS: ASSESSMENTS IN THE EUROPEAN UNION AGRICULTURAL REGIONS THROUGH MACHINE LEARNING APPROACHES

Vítor João Pereira Domingues MARTINHO

Coordinator Professor with Habilitation, Agricultural School (ESAV) and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu (IPV), Portugal

vdmartinho@esav.ipv.pt

Abstract

There is an enormous potential to produce bioenergy from agriculture, forestry and other land use in the European Union (EU) farms. The agricultural sector in the EU member-states has conditions to increase the contributions of renewable energies through better use of the residues and the production of energy crops. Nonetheless, the profitability of these alternative agricultural outputs, in some circumstances, and the need for land for food production, for example, have been obstacles to effective positioning of the EU farms as sources of bioenergy. From this perspective, this study intends to assess the current context of the energy crops in the farms of the EU agricultural regions and identify a model that supports the prediction of these frameworks. For that, data from the Farm Accountancy Data Network (FADN) were considered for the year 2020. This statistical information was analysed through machine learning approaches, namely those associated with multilayer perceptron (MLP) algorithms from the artificial neural networks (ANN) methodologies. The results from these data show that energy crops do have not relevant importance in the European Union farms. On the other hand, when these crops appear, they are produced by larger farms, with greater competitiveness and which receive more subsidies.

Keywords: Agriculture 4.0, Artificial Neural Networks, Multilayer Perceptron

JEL classification: C45, Q12, Q42

pp. 29-42

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