Correction: Spatial disparities altered the determinants of biomass supply intention for clean energy: policy implications

Résumé

The success of bioenergy promotion and development significantly depends on people's awareness, acceptance, and willingness to adopt, use, and pay for such energy (Liobikienė et al., 2021a). In the field of bioenergy, social opinion analysis generally seeks to identify and explain the determinants of respondents' attitudes and intentions or willingness towards bioenergy adoption, encompassing aspects ranging from biomass supply to energy consumption. This is typically a theory-driven analysis of key human internal dimensions (attitudes, intentions, subjective norms, and perceived behavioral control), social dimensions (socioeconomic or sociodemographic characteristics), and external dimensions (energy price and accessibility). Numerous authors have extensively explored the determinants of biomass supply decisions (Al-Mulali et al., 2016;Baležentis et al., 2019;Liobikienė et al., 2021a).However, they overlooked the potential spatial variation of these factors, which is receiving increasing attention in bioenergy research (Brunson and Shindler, 2004). According to the first law of geography (Miller, 2004), the geographical distance between locations is an important factor that can affect stakeholders' decision-making and their behavior. As a result, the intentions to supply biomass resources for bioenergy may differ among geographically distant regions primarily due to variations in resource availability, competing resource uses, socioeconomic conditions, and cultural factors. This implies that spatial variation in biomass supply intention could also correlate with key intention predictors such as attitude, environmental concern (EC), knowledge of renewable energy (KRE), subjective norms (SN), and perceived behavioral control (PBC). Investigating this aspect in the field of the bioenergy is important for several reasons. First, it can help policymakers to identify the specific needs and challenges for each region and design more effective and region-specific initiatives to promote bioenergy adoption (He et al., 2022). Second, it can guide the development of supportive policy frameworks that consider unique spatial contexts, enabling the creation of incentives, infrastructure, and supportive measures that encourage bioenergy production (Skevas et al., 2018;Khanna et al., 2021). Third, by considering the specific spatial characteristics, such as biomass resources availability, land use patterns, and socioeconomic factors, stakeholders can make informed decisions regarding the development of bioenergy projects, distribution networks, and infrastructures (Thomas et al., 2014;Kabir, 2021).To address the question of spatial variation or spatial interdependence in decision-making within the field of bioenergy, various methodological frameworks have been employed in the literature, including correlation tests (Brunson and Shindler, 2004), Bayesian frameworks (Holloway et al., 2007;Skevas et al., 2018), spatially lagged explanatory variable (SLX) and spatial Durbin error probit models (SDEM), as discussed by Storm et al. (2015). However, we did not use any of these approaches because Bayesian frameworks, SLX, and SDEM are more commonly employed for spatial interdependence analysis, while simple correlation tests fail to account for multiple causal relationships, including mediation effects.In this study, we employed Structural Equation Modeling (SEM) based on the Theory of Planned Behaviour (TPB) as a methodological framework. This framework is powerful in designing a structural model that links the three human dimensions (psychological, social, and external) to test their direct or mediation effects on intentions or behaviors (Ajzen, 1991). The TPB postulates that intention is positively predicted by three main latent variables: attitude, SN and PBC (Ajzen, 1991;Fishbein and Ajzen, 2011). The TPB has been widely used in the field of bioenergy (Collins and Carey, 2007;Leitch et al., 2013;Yee et al., 2022) and continues to evolve through incorporation of new predictor variables such as EC, KRE and sociodemographic or socioeconomic factors such as age, sex, and income (Loera et al., 2022).Based on this background, we expanded the TPB initial model by adding two latent factors (EC and KRE) and two socioeconomic variables (age and income) in this study. These new variables are increasingly being used as key predictors of attitude and intentions in the bioenergy field (Lai et al., 2015).The main objective of this study is to examine whether the intentions to supply forestry residues for clean energy purposes and the factors determining these intentions are influenced by spatial disparities. Spatial disparities refers to all inequality in the distribution of geographical variables across different spatial units (Kanbur and Wan, 2006). In our study context, the concept of "spatial disparities" refers to the divergence in multiple socioeconomic factors such as income, economic potential, education, poverty, urbanization, and competition for the use of forestry residues between two distant geographical regions (northern vs. southern region) in the Republic of Benin. The northern region is less urbanized and has a lower economic potential and education level compared to the south. However, it has a higher poverty level and is a significant source of woody biomass (stemwood, logging and wood processing byproducts, and stumps) due to its higher forest cover compared to the south (Ahononga et al., 2020;Biaou et al., 2021).The TPB has been instrumental in understanding the multiple factors that drive human behaviors. Particularly, in the bioenergy sector, researchers frequently employ TPB to identify and enhance elements that encourage public intentions to support bioenergy initiatives. The TPB was first proposed by Ajzen (1991) and can be defined as an updated model of the Reason Action Theory. The TPB postulates that the intentions of a person is an accurate measure of his behaviors and that his intention is determined by his attitude, SN, and PBC. In this line, the theory posits that people evaluate the salient consequences (advantages and drawbacks) of the behaviors they want to perform before engaging in them and they are more likely to perform behaviors associated with desirable advantages (Ajzen and Fishbein, 1975;Bang et al., 2000a). A behavior's performance by a person is also determined by his motivations to comply with related people's opinions and his ability to manage barriers against that behavior and opportunities.Mathematically, attitude is defined as the sum of the product of the beliefs (bi) about the behavioral object and the valence evaluation (ei) of the beliefs (Figure 1). The theory posits that a person with strong beliefs about the advantages of a particular behavior have a higher intention to perform it. One can mathematically define SN as the sum of the product of what other people think about the behaviors that a person intends to perform (NBi) and his motivations to comply with those beliefs (MCj). The theory also posits that persons with strong normative beliefs are more likely to perform a behavior than not perform it. The PBC can be defined as the level of confidence of a person to perform a behavior. It is a person's ability to challenge hindrances against behavioral performance by using the internal facilitators (skills, knowledge, and experience) and external facilitators (information and opportunities) of a behavior (Ajzen, 1991). PBC is the sum of the product of a person's control of his behaviors (ci) and his perceived control (pi). As predicted by TPB, persons with strong beliefs about particular behavioral facilitators are more likely to perform them.We expanded the Ajzen (1991) initial TPB model by including EC and KRE as key latent variables, as well as age and income (monthly income) as socioeconomic variables (Figure 2).In fact, previous authors have reported a positive correlation between biomass supply intentions and EC or KRE (Lai et al., 2015;Ali et al., 2023). Positive correlations were also found between both EC, KRE and attitude (Liobikienė et al., 2021a). It was also found that the effect of EC or KRE on intentions may be mediated by attitude (Li et al., 2019). Age and income were previously used as key socioeconomic and demographic predictors of intentions (Liu et al., 2013;Dagiliūtė, 2023). Age-intention relationship is reportedly negative across the literature (Joshi and Mehmood, 2011;Liu et al., 2013) whereas income was positively correlated with intentions (Karanja & Gasparatos, 2019;Liu et al., 2013). Based on the predictions from the TPB and previous findings, we tested the following four hypotheses. H1: Respondents with a more positive attitude towards forestry residues supply for clean energy production, a stronger alignment with SN, and greater PBC, particularly, those who are environmentally friendly or have more KRE, are likely to exhibit higher intentions to supply forestry residues for clean energy production. H2: Demographic characteristics, such as having a higher income or being younger, are positively associated with the intentions to supply forestry residues for clean energy production.H3: The effect of EC and KRE on the intentions to supply forestry residues for clean energy production is mediated by the attitude towards these intentions.H4: Spatial disparities have a significant influence on the determinants of people intentions to supply forestry residues for clean energy production.The study was carried out in the Republic of Benin, a West African country located between 6°15' and 12°25' N and 0°40' and 3°45'E (Figure 3). The country covers an area of 114 763 km² with a population density of 87.2 inhabitants per km 2 (11 186 785 inhabitants) in 2017 (INSAE, 2019). The south is more populated (57.60 inhabitants per km 2 ) compared to the north (29.59 inhabitants per km 2 ). About 42% of the population of Benin is poor, with a higher rate of poverty in the north (60 % of the population) compared to the south (35% of the population). The level of education is higher in the south, with 76% of the population having received at least a primary education, in contrast to the north, where only 40% have reached the same educational level (INSAE, 2015;INSAE and ICF, 2019). Approximately half of the country's households (50%) used fossil fuel for lighting, with a higher rate in the south (59.22%) compared to the north (20.82%). About a third of the country's households (33.42%) purchase electricity from the government (Agbandji et al., 2020). The south is more electrified (34.55% of the households) than the north (18.52%) (Yatokpa et al., 2010).Charcoal is commonly used for cooking across the country, but it is more commonly used in the south (28.6% of households) than in the north (18.97%). The use of renewable energy sources, such as solar power and residue-derived energy (from forestry and is The from in the to in the et al., 2006). are in the north and in the south. The is of The north has a higher forest and of the country's forest cover (Ahononga et al., 2020). West African Benin depends on with of fossil and of power from and (Yatokpa et al., only a of households have to electricity (Yatokpa et al., a of and wood residues is The area in the north and in the in of forestry residues production in Benin Republic (Figure first a of which were to measure SN, PBC, EC, KRE, and The also two key socioeconomic variables (age and These were using a in that our the key for and the was first to an for the of the a was by the to a of This was to test the of the and to that were not or more than a question et al., The updated was on a using a for et al., a by the to a of This is for et al., 2013). the is to the of by various and and the in where forestry residues are and wood processing stumps) across the These were by the of the of wood The were based on the of wood processing and in the In each we the to residues and and and and and of the on each sociodemographic sex, education and income) and the we a factor analysis using the with in to out the of and their factors from the initial of that latent variables were in our TPB we that a factor model was to the of in the This was tested in using the The model is the of the is greater than Based on the first we a using that are and a greater than on each factor this with the that each factor a of three of et al., and 2021). the internal of each factor using The was in using the and 2020). greater than that the is to measure the The and the test of were also to evaluate the of the test of was using the The desirable for the be greater than was using the from any factor analysis was to the factor is a theory-driven that seeks to test whether the between the and factors are it is about a population with the et al., the least to The was using the in we a using the to test the structural relationship between the first the for each geographical region and it using from the two In the the model was the In the of the we tested the effect of attitude, SN, PBC, KRE, and EC on intentions and the effect of KRE and EC on also the mediation effect of KRE and EC through in we used the least for the model the of each model to the model explanatory The of the and models was based on the by & and and the than were on in the north to in the were and to the the across the In the northern of the were compared to of of were in the south, compared to of Approximately and of the reached and primary of them reached and did not have any were more with education in the south than in the north The income of was on This income is lower in the north compared to the south with our a model was to the of variables in our The factors for of the in the 1). a that our model is The test was also significant The of the was and for all the factors to first factor for of the in our and is related to PBC 1). 2 of the and is related to KRE of the and attitude of the and is to EC of the and the that the intention to supply woody of the and is related to analysis that opinions about the of forestry residues for bioenergy all the received from the to 1). to the attitude strong beliefs that the use of residues for power or production the of energy and for people the on intentions, to use for and supply the residues for bioenergy they to supply the residues any and supply them for as as knowledge about biomass and bioenergy was also understanding of biomass and bioenergy was also that bioenergy is environmentally friendly and its are to fossil also the that the of bioenergy on their Respondents more about the the of and policy by the education and for power In of normative they to supply the residues of public opinions to and with the public opinions that using residues for bioenergy purposes control such strong that they could their and support any bioenergy in of and to the for the and were and a The and were and The was the as were the and the The from to a between and latent spatial disparities were by the from the two we found significant correlations between intentions and several predictor the model predictors explain of the in respondents' This the explanatory power of this intentions were positively correlated with attitude, PBC, and income (Figure in the This our first and and that with a more positive attitude towards forestry a higher PBC and a higher income are likely to have a higher intention to supply forestry found a negative correlation between intentions and KRE (Figure in the which contrast with our that who are more about renewable energy are likely to have a greater willingness to supply woody The that a stronger alignment with SN, a higher environmentally or being are positively associated with the intentions was not In fact, significant correlation was found between intentions and SN, EC, and age (Figure in the EC was positively correlated with attitude but not with KRE (Figure in the In of KRE and SN negative while the relationship between age and income was positive (Figure the region-specific we found explanatory power of the model across the geographical with higher in the north than in the south found of spatial variation in this study. KRE to intentions in the north but a positive with intentions in the south (Figure the KRE effect on attitude also spatial with a positive effect in the north (Figure and a negative effect in the south (Figure spatial variation was found regarding the effects of PBC, EC, SN, attitude, income, and age on The intentions to supply the residues were positively predicted by PBC and income across geographical regions (Figure in the The effects of EC, SN, and attitude were not significant across geographical also found spatial variation in the between KRE and EC with a positive in the north (Figure and a negative in the south (Figure variables SN, KRE, and age significant effect on intentions across the spatial (Figure our that the determinants of the intention to supply forestry residues for clean energy are influenced by spatial the mediation EC and KRE effect on intention through attitude 3). This our third that the effect of EC and KRE on the intention to supply the residues is mediated by study the TPB to the intentions to supply forestry residues for bioenergy purposes and an expanded TPB model to identify the determinants of intention and whether the model is by spatial to of all our TPB models were the of and a strong explanatory power of the models and the of the first that with a more positive attitude, a stronger alignment with SN, and greater PBC, particularly, those who are environmentally friendly or have more KRE, are likely to have a higher intention to supply forestry residues for clean energy that people's intentions to supply the residues is positively predicted by attitude and PBC, while it a negative and with KRE and SN, This our first and is with the predictions from the theory of behavior (Ajzen, 1991). The positive effect of attitude and PBC on intention was reported by other in the literature and et al., et al., 2019). According to the behavioral attitude theory and Ajzen, attitude is as the first important variable in people's positively intentions it is by et al., attitude and EC were positively we that EC be the key of the positive effect of attitude on intention in this study. This the attitude of EC towards the social support of This with in the literature (Liobikienė et al., and that people's about the their intention to supply residues for bioenergy positive relationship between PBC and intention with the from the theory of behavior (Ajzen, and stakeholders' to supply the residues for bioenergy our that are that public beliefs not their opinions to supply the also strong willingness to support renewable energy initiatives by their in renewable energy projects, and residues for This their strong ability to their intentions and on that may make renewable energy to our in the first KRE to This is because an in KRE people of the of bioenergy, such as its purchase potential on and land use and et al., correlation between SN and intention was not to our in the first This the of social and on this the from the theory of behavior (Ajzen, that perceived opinions may or not to behavioral SN an in this study, because are to the opinions of their Based on the theory of this may be by the that of the have needs than needs not social or social have influence on respondents' decisions to supply the could be that the concept of renewable energy is only educational and has to cultural the is to that of previous et al., et al., et al., significant links were reported in several and et al., et al., et al., et al., that demographic characteristics, such as having a higher income or being younger, are positively associated with the intention to supply forestry residues for clean energy the of a positive effect of income but to support the effect of The is that with higher can purchase as a for the As reported by and higher to willingness to support renewable energy was also previously reported (Karanja and Gasparatos, et al., et al., 2023). The correlation between intention and age with the opinion that education and environmental can the willingness to support bioenergy at a age et al., 2021). The relationship between intention and age to be likely due to the to renewable energy in educational as well as the about them in This is by the of et al. who correlation between respondents' age and their willingness to use based on human whether the determinants of the intention to supply the residues any spatial that intention predictors are by spatial disparities found a in the effect of KRE between the two but spatial disparities did not the other KRE a effect in the north but a positive effect in the south. KRE significantly influenced but this effect from positive in the north to negative in the south. that the higher education level in the south was the main for this spatial In education can people's about the of using bioenergy and et al., which their intentions to support bioenergy can also KRE by them with knowledge on renewable bioenergy, including renewable and potential uses, which may people's confidence in bioenergy as a to energy (Karanja and Gasparatos, 2019). The spatial variation of the KRE effect on attitude may be associated with disparities in geographical between The negative influence of KRE on attitudes in the south may from this higher economic potential, which to higher competition for forestry residues for various compared to the Spatial disparities did not the effect of PBC and income, because socioeconomic and between the two regions (Skevas et al., The positive effect of income the of people's for a adoption of bioenergy et al., 2021). This the of to make bioenergy more et al., The and positive effect of PBC across regions may be the of which their ability to any hindrances or challenges in the residues for bioenergy et al., et al., social support and from and and bioenergy respondents' perceived which have their intentions to supply the residues and and and et al., It may also be by EC, as the two variables positive several authors reported that people who are environmentally may a to support bioenergy and and and et al., which may their perceived employed based on the TPB to the determinants of the intentions to supply forestry residues for bioenergy and test the of spatial variation on the determinants of the intentions to supply residues for important first is that spatial variation is the intentions to supply residues is determined by attitude, income, PBC, and The is that spatial variation is we found a in KRE effect between while PBC and income a positive effect of study has important policy The effect of income implies that policymakers to encourage in bioenergy This people's control beliefs and their willingness to supply the residues for are to the northern region of Benin Republic while because of the higher poverty in this region that may the adoption of renewable energy due to barriers et al., et al., 2022). also and to income and enhance their control the positive between EC and PBC implies that people's environmental can their control beliefs and their intentions to supply the The spatial disparities on the KRE effect is that can be used by policymakers to to each geographical policymakers can people's attitude in the north through KRE can also residues supply intentions in the south by people's KRE in the south. The success of these the incorporation of renewable energy educational and through and is that on biomass supply intentions for bioenergy consider the spatial of variation in intentions to make accurate This more bioenergy and the towards behavior and renewable energy by these policy related to education, incentives, and the promotion and adoption of bioenergy can be to a and more energy our knowledge about the various factors that influence people's decisions to make biomass for clean energy purposes is to energy in the field of bioenergy have important the determining factors of biomass supply intention but spatial disparities their This overlooked aspect the of This study significantly to the field by whether spatial disparities the determinants of biomass supply intentions for clean that by for spatial disparities in the biomass supply study, researchers in the field are to make accurate and policymakers with that may help them more and effective to promote clean energy study that considering spatial disparities can help policymakers region-specific that can the adoption of bioenergy and energy in geographical

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