Regional Sustainability ›› 2025, Vol. 6 ›› Issue (3): 100225.doi: 10.1016/j.regsus.2025.100225cstr: 32279.14.REGSUS.2025015
• Research article • Next Articles
Fábio DE OLIVEIRA NEVESa,*(
), Eduardo Gomes SALGADOb, Mateus CURYb, Jean Marcel Sousa LIRAc, Breno Régis SANTOSb
Received:2024-11-14
Accepted:2025-06-01
Published:2025-06-30
Online:2025-07-08
Contact:
Fábio DE OLIVEIRA NEVES
E-mail:fabiooneves@gmail.com
Fábio DE OLIVEIRA NEVES, Eduardo Gomes SALGADO, Mateus CURY, Jean Marcel Sousa LIRA, Breno Régis SANTOS. Challenges and opportunities in the energy transition of agribusiness: A deep dive into the rebound effect in Latin America[J]. Regional Sustainability, 2025, 6(3): 100225.
Table 1
Data source of the selected variables."
| Independent variable | Unit | Description | Period | Database |
|---|---|---|---|---|
| CO2 emissions (CO2) | 103 kg/a | Analysis of CO2 emissions from agribusiness | 2010-2022 | World Bank ( |
| CH4 emissions (CH4) | 103 kg/a | Assessing the impact of livestock waste management | 2010-2022 | World Bank ( |
| NO2 emissions (NO2) | 103 kg/a | Understanding the impact of agricultural practices on greenhouse gas emissions | 2010-2022 | World Bank ( |
| Non-renewable energy (EnR) | kJ/(106 kW•h) | Understanding the impact of traditional energy on the sector | 2010-2022 | World Bank ( |
| Renewable energy (ER) | kJ/(106 kW•h) | Understanding the progress of the transition to cleaner and more sustainable energy | 2010-2022 | World Bank ( |
| Pesticides (PS) | kg/hm2 | Understanding how improving energy efficiency reduces the application of these chemical products and its impact on the environment | 2010-2022 | World Bank ( |
| Water consumption (WV) | m³ | Identification of crops with high water consumption | 2010-2022 | World Bank ( |
| Water conservation practices (WC) | % | Understanding how energy transition promotes more efficient water management measures | 2010-2022 | World Bank ( |
| Income and well-being of farmers (IWF) | USD or % | Evaluating the impact of energy transition of agribusiness on the income and well-being of farmers | 2010-2022 | World Bank ( |
| Agricultural employment (EA) | Number of workers or % | Evaluating the impact of energy transition of agribusiness on rural employment | 2010-2022 | World Bank ( |
| Irrigation efficiency (IF) | L/hm2 or % | Evaluating the effect of water resource utilization in the irrigation system | 2010-2022 | World Bank ( |
Fig. 1.
Relationship between service industry energy consumption and other product energy consumption. U0, initial energy consumption curve; U1, final energy consumption curve; Q0, initial energy consumption in the service industry before the improvement of energy efficiency; Q1, expected energy consumption after the improvement of energy efficiency; Q2, actual observed energy consumption after the improvement of energy efficiency. The negative values on the Y-axis (other product energy consumption) represent theoretical outcomes of the utility curves used in the model. These values do not reflect actual negative energy consumption, which would be physically implausible."
Table 2
Types of the rebound effect."
| Type | Rebound effect value | Implication |
|---|---|---|
| Backfire effect | >100.00% | Unintended consequences that occur when the initial action leads to an effect contrary to expectations |
| Total rebound effect | 100.00% | Full rebound effect after an intervention |
| Partial rebound effect | 0.00%-99.00% | Partial recovery effect |
| Zero rebound effect | 0.00% | Absence of recovery effect |
| Super conservation effect | <0.00% | Exaggerated reaction that amplifies the initial effect |
Table 3
Bootstrap resampling validation results for the five countries."
| Country | Sector | t-statistic value | P-value |
|---|---|---|---|
| Brazil | Sugarcane | 32.25 | <0.0001 |
| Livestock | 45.62 | <0.0001 | |
| Mexico | Livestock | 23.78 | <0.0001 |
| Argentina | Soybean | 12.84 | <0.0001 |
| Livestock | 29.83 | <0.0001 | |
| Colombia | Coffee | 24.84 | <0.0001 |
| Uruguay | Livestock | 67.84 | <0.0001 |
Table 4
Reliability of the Double-Log regression model for five countries."
| Country | Sector | F-value | χ2 | AIC | VIF | R2 |
|---|---|---|---|---|---|---|
| Brazil | Sugarcane | 3.23 | 89.43 | 231.23 | 5.78 | 0.97 |
| Livestock | 4.92 | 96.24 | 134.65 | 1.83 | 0.95 | |
| Mexico | Livestock | 3.92 | 82.92 | 112.72 | 10.23 | 0.61 |
| Argentina | Soybean | 5.23 | 70.23 | 149.82 | 3.92 | 0.90 |
| Livestock | 9.46 | 81.98 | 201.52 | 6.57 | 0.86 | |
| Colombia | Coffee | 7.39 | 87.94 | 169.49 | 7.34 | 0.77 |
| Uruguay | Livestock | 7.48 | 76.94 | 201.03 | 5.67 | 0.89 |
Table 5
Residual diagnosis results for five countries."
| Country | Sector | Levene test | Durbin-Watson test | Shapiro-Wilk test | Anderson-Darling test | Dixon test |
|---|---|---|---|---|---|---|
| Brazil | Sugarcane | 20.12 | 30.19 | 22.14 | 43.02 | 12.83 |
| Livestock | 9.32 | 25.81 | 72.91 | 56.13 | 34.20 | |
| Mexico | Livestock | 54.23 | 53.90 | 16.42 | 9.23 | 42.10 |
| Argentina | Soybean | 62.45 | 24.83 | 33.68 | 12.90 | 43.87 |
| Livestock | 12.32 | 12.93 | 9.54 | 32.01 | 28.35 | |
| Colombia | Coffee | 37.21 | 32.84 | 11.72 | 23.45 | 6.12 |
| Uruguay | Livestock | 8.29 | 77.01 | 13.63 | 76.56 | 31.63 |
Table 7
Double-Log regression model results in the sugarcane and livestock sectors of Brazil."
| Sector | Dependent variable | Independent variable | LLC test | ADF-Fisher test | PP-Fisher test |
|---|---|---|---|---|---|
| Sugarcane | Energy consumption | ln(EnR)*** | -21.94 | 103.12 | 142.35 |
| ln(EA)*** | -24.32 | 101.56 | 276.03 | ||
| ln(IF)*** | -17.83 | 136.73 | 156.23 | ||
| Livestock | Energy consumption | ln(CH4)*** | -12.45 | 104.23 | 345.21 |
| ln(IWF)*** | -21.94 | 89.54 | 89.03 | ||
| ln(WV)*** | -13.92 | 104.83 | 194.83 | ||
| ln(WC)*** | -9.73 | 110.76 | 201.84 |
Table 9
Double-Log regression model results in the soybean and livestock of Argentina."
| Sector | Dependent variable | Independent variable | LLC test | ADF-Fisher test | PP-Fisher test |
|---|---|---|---|---|---|
| Soybean | Energy consumption | ln(PS)*** | -34.12 | 62.29 | 329.54 |
| ln(NO2)*** | -5.32 | 80.54 | 283.53 | ||
| ln(ER)*** | -10.85 | 105.23 | 157.28 | ||
| ln(EA)*** | -8.92 | 81.94 | 245.83 | ||
| Livestock | Energy consumption | ln(ER)*** | -10.23 | 67.82 | 100.81 |
| ln(IWF)*** | -17.42 | 90.10 | 102.83 | ||
| ln(WV)*** | -9.85 | 78.02 | 219.28 |
Table 10
Double-Log regression model results in the livestock sector of Uruguay."
| Sector | Dependent variable | Independent variable | LLC test | ADF-Fisher test | PP-Fisher test |
|---|---|---|---|---|---|
| Livestock | Energy consumption | ln(EnR)*** | -11.92 | 102.83 | 117.02 |
| ln(IWF)*** | -12.05 | 98.19 | 312.83 | ||
| ln(IF)*** | -10.92 | 104.29 | 110.93 |
Table 12
Analysis of variance (ANOVA) test results for the common independent variables in each country pair."
| Country pair | Common independent variable | P-value | Country pair | Common independent variable | P-value |
|---|---|---|---|---|---|
| Brazil-Mexico | ln(IF) and ln(EnR) | 0.03 | Mexico-Uruguay | ln(IF) and ln(EnR) | 0.02 |
| Brazil-Argentina | ln(IF) and ln(EnR) | 0.02 | Mexico-Colombia | ln(IF) | 0.03 |
| Brazil-Uruguay | ln(IF) and ln(EnR) | 0.04 | Argentina-Uruguay | ln(IF) and ln(EnR) | 0.04 |
| Brazil-Colombia | ln(IF) | 0.01 | Argentina-Colombia | ln(IF) and ln(EnR) | 0.02 |
| Mexico-Argentina | ln(IF) and ln(EnR) | 0.05 | Uruguay-Colombia | ln(IF) | 0.01 |
Table 13
Bonferroni-adjusted Post Hoc test results for independent variables."
| Independent variable | F-value | P-value | Independent variable | F-value | P-value |
|---|---|---|---|---|---|
| ln(PS) | 7.12 | <0.001 | ln(WV) | 5.34 | 0.002 |
| ln(NO2) | 8.34 | <0.001 | ln(EnR) | 8.56 | <0.001 |
| ln(ER) | 6.78 | 0.002 | ln(IF) | 7.23 | <0.001 |
| ln(EA) | 7.89 | <0.001 | ln(CH4) | 6.45 | <0.001 |
| ln(IWF) | 6.45 | 0.003 |
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