
Rural areas are essential to moving towards the necessary sustainable energy transition and climate change mitigation through renewable energy (RE) technologies. However, RE planning and decision-making in rural locations have not been developed to date with a focus on the local level and accompanied by a careful and thorough assessment of the simultaneous availability of alternative RE sources in a specific territory. Quite differently, RE investments in rural locations have been primarily driven by the interests of large power utilities to exploit a particular RE source, with benefits escaping from the rural economies to end up in the income statements of those large corporations. There is a need to approach RE planning at the municipal scale considering the availability of alternative RE sources. This study suggests the development of a rural RE potential index that could help in the identification of appropriate locations for the implementation of hybrid renewable energy systems (HRESs). The construction of a composite indicator to measure rural RE potential is exemplified through a case study that deals with ten indicators in the context of Galician rural municipalities, involving different RE potentials and some technical or regulatory constraints. Equal weighting and Principal Component Analysis are considered alternative methods for the index construction. Municipalities are the relevant local decision level where energy policy should be focused in order to diversify both the RE mix and the investor base. The proposed index could be the basis for future analyses aimed at optimizing the design and implementation of HRESs in rural environments at a local-regional-national scale.
Citation: Vanessa Miramontes-Viña, Noelia Romero-Castro, M. Ángeles López-Cabarcos. Advancing towards a sustainable energy model, uncovering the untapped potential of rural areas[J]. AIMS Environmental Science, 2023, 10(2): 287-312. doi: 10.3934/environsci.2023017
[1] | Yan Ling Fu, Wei Zhang . Some results on frames by pre-frame operators in Q-Hilbert spaces. AIMS Mathematics, 2023, 8(12): 28878-28896. doi: 10.3934/math.20231480 |
[2] | Gang Wang . Some properties of weaving K-frames in n-Hilbert space. AIMS Mathematics, 2024, 9(9): 25438-25456. doi: 10.3934/math.20241242 |
[3] | Sergio Verdú . Relative information spectra with applications to statistical inference. AIMS Mathematics, 2024, 9(12): 35038-35090. doi: 10.3934/math.20241668 |
[4] | Cure Arenas Jaffeth, Ferrer Sotelo Kandy, Ferrer Villar Osmin . Functions of bounded (2,k)-variation in 2-normed spaces. AIMS Mathematics, 2024, 9(9): 24166-24183. doi: 10.3934/math.20241175 |
[5] | Chibueze C. Okeke, Abubakar Adamu, Ratthaprom Promkam, Pongsakorn Sunthrayuth . Two-step inertial method for solving split common null point problem with multiple output sets in Hilbert spaces. AIMS Mathematics, 2023, 8(9): 20201-20222. doi: 10.3934/math.20231030 |
[6] | Osmin Ferrer Villar, Jesús Domínguez Acosta, Edilberto Arroyo Ortiz . Frames associated with an operator in spaces with an indefinite metric. AIMS Mathematics, 2023, 8(7): 15712-15722. doi: 10.3934/math.2023802 |
[7] | Abdullah Ali H. Ahmadini, Amal S. Hassan, Ahmed N. Zaky, Shokrya S. Alshqaq . Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19. AIMS Mathematics, 2021, 6(3): 2196-2216. doi: 10.3934/math.2021133 |
[8] | Messaoud Bounkhel . V-Moreau envelope of nonconvex functions on smooth Banach spaces. AIMS Mathematics, 2024, 9(10): 28589-28610. doi: 10.3934/math.20241387 |
[9] | Jamilu Adamu, Kanikar Muangchoo, Abbas Ja'afaru Badakaya, Jewaidu Rilwan . On pursuit-evasion differential game problem in a Hilbert space. AIMS Mathematics, 2020, 5(6): 7467-7479. doi: 10.3934/math.2020478 |
[10] | Yasir Arfat, Muhammad Aqeel Ahmad Khan, Poom Kumam, Wiyada Kumam, Kanokwan Sitthithakerngkiet . Iterative solutions via some variants of extragradient approximants in Hilbert spaces. AIMS Mathematics, 2022, 7(8): 13910-13926. doi: 10.3934/math.2022768 |
Rural areas are essential to moving towards the necessary sustainable energy transition and climate change mitigation through renewable energy (RE) technologies. However, RE planning and decision-making in rural locations have not been developed to date with a focus on the local level and accompanied by a careful and thorough assessment of the simultaneous availability of alternative RE sources in a specific territory. Quite differently, RE investments in rural locations have been primarily driven by the interests of large power utilities to exploit a particular RE source, with benefits escaping from the rural economies to end up in the income statements of those large corporations. There is a need to approach RE planning at the municipal scale considering the availability of alternative RE sources. This study suggests the development of a rural RE potential index that could help in the identification of appropriate locations for the implementation of hybrid renewable energy systems (HRESs). The construction of a composite indicator to measure rural RE potential is exemplified through a case study that deals with ten indicators in the context of Galician rural municipalities, involving different RE potentials and some technical or regulatory constraints. Equal weighting and Principal Component Analysis are considered alternative methods for the index construction. Municipalities are the relevant local decision level where energy policy should be focused in order to diversify both the RE mix and the investor base. The proposed index could be the basis for future analyses aimed at optimizing the design and implementation of HRESs in rural environments at a local-regional-national scale.
Quantitative, informative and applicable models have long been proven beneficial to assist law enforcement to curb urban crimes. Following the seminal work (see reference [18]) on the mathematics of agent-based models for residential burglary, many works have been done on mathematical crime modeling and prediction, see e.g. references [1], [3], [7], [9], [12], [13], [14], [15], [16], [18], [19] and [20], and the references cited therein, and in [18].
There are roughly two classes of crime models. One is agent-based aiming to describe the individual activities, and the other is event-based without specifying the individual agents, aiming to predict the patterns of observed events (see e.g. references [15] and [16]). Here we address the first class of models.
In reference [18], "hotspots", clusters of residential burglary well documented in real life, are quantitatively studied for the first time. A discrete lattice grid is imposed, upon which residents are distributed each labeled with its level of attractiveness, and burglary agents walk over sites seeking for targets. Burglary dynamics is coupled with the environment variable based on the repeat and near-repeat victimization and the broken-windows effects. These notions maintain that a previously burgled house and its neighbors are more attractive to potential offenders, as an environment encouraging further illegal activities is more likely to be created by past crimes with visible signs (see the relevant references in references [18], [19] and [20]).
In reference [18] the time steps are set as deterministic and all types of events occur at fixed regular intervals. Random arrivals are incorporated later in references [19] and [20], governed by stochastic clocks generated from Poisson processes. In reference [19], there is one uniform Poisson-clock, and in reference [20] independent Poisson clocks are assigned to each individual agent. To summarize, these agent-based models are referred to as DTS (deterministic-timestep) Model (see reference [18]), SSRB (stochastic-statistical residential burglary) Model (see reference [20]), and SSRB-IPC (independent Poisson-clock) Model (see reference [20]), respectively. Continuum analogues parallel to these models are derived as a mean-field limit in reference [18] or a potential hydrodynamic limit in references [19] and [20], both of which lead to the same coupled deterministic reaction-diffusion equation system.
The models mentioned above serve as the first-generation agent-based criminal behavior models, whose prototype will be referred to as First-Generation Model. They are stochastic models with a continuum limit that can be viewed as a statistical average. The continuum limit is simpler to analyze and simulate by computer, and when total population is sufficiently large, the discrete and continuum simulations exhibit similar hotspot dynamics. However, as agent number decreases, the discrete simulations exhibit more transient hotspot regimes, generating the finite size effects. Quantitative analysis in references [19] and [20] show that these effects arise due to stochasticity of the system, and criminals act randomly in reality. It is called for that we combine the strengths of First-Generation Model and its continuum limit to build a second-generation criminal behavior model.
In this work, we construct a stochastic multi-scale criminal behavior model with hybrid dynamics, where agent actions and environment variables are set on different spatio-temporal scales. This model will be referred to as M-IPC (Multiscale-Independent-Poisson-Clock) Model. In contrast, the clocks in First-Generation Model (deterministic and random) all have rates at the same order of magnitude. Here we assume instead that the environment variables evolve as the fast component continuously in time and in space, while criminal agents move along discrete lattice grids following independent Poisson clocks on slow time scales. The evolution of the environment variables can be sociologically interpreted as the spreading of information, which we assume to be in a continuous mode. That is, the rate of change of information at each spatial point is "fast" while the amount of change is infinitesimal during each infinitesimally short time period. And we adopt suitable temporal-spatial scaling to make the change of information at each spatial point over a time period to have the same order of magnitude. The result is a partial differential equation whose parameter depends upon agent distributions, which switches values according to the Poisson jumps governing agent activities. The simulation cost of agent-based M-IPC Model is vastly reduced compared to the SSRB-IPC model. Moreover, transient hotspot regimes as well as stationary hotspot regimes arise in the simulations. And our M-IPC Model with the multiscale setup can also be applied to types of crimes other than residential burglary, e.g. drug smugglings.
The separation of scales not only facilitates simulations, but also brings in the mathematical framework of piecewise deterministic Markov processes (PDMP), while First-Generation Model evolves according to (discrete or continuous time) Markov processes. A martingale approach similar to that developed in references [19] and [20] is applicable. The martingale formulation can be used to represent the model as the sum of a deterministic and a stochastic part, which will be useful for further study of hotspot dynamics. PDMP, also referred to as the iterated random functions (see e.g. reference [6]), hybrid stochastic processes, or jumping Markov processes (see e.g. reference [11]), has been extensively studied (see e.g. references [2], [5], [8], [10] and [17], and the references cited therein). Also PDMP has been widely applied in physics, chemistry, biology and other fields in natural science (see e.g. references [2], [8] and [17], and the references cited therein). However, as far as we know, this is the first time that PDMP is applied to crime modeling, and our methodology can be useful for quantitative study in social and behavioral science in general.
The paper is organized as follows. We first introduce the set-up of agent-based M-IPC Model (Section 2), where the slow and the fast components are described in Sections 2.2 and 2.3, respectively. The computer simulations are shown in Section 3, where both transient and stationary hotspot regimes are displayed. We review the general characterizations of PDMP and apply them to our model (Section 4.1). Finally the martingale representation of our model is established in Section 4.2.
We start with a domain
1With minor changes we can also consider e.g. the Dirichlet boundary conditions, which is more realistic.
(ⅰ) Natural districts corresponding to those in an urban setting. For example, in Los Angeles, we can consider the natural counties as what those districts correspond to.
(ⅱ) Artificial districts to facilitate the collecting of data etc.
Attached to each
ˉAK=∫DKA(x)dx, | (1) |
We also assume that
A(x,t)=B(x,t)+A0(x), | (2) |
where
(NK(t)),B(x,0))=(N0K,B0(x)). | (3) |
Following SSRB-IPC Model in reference [20], three types of independent Poisson clocks govern the time increments of arrivals of events, and all the Poisson clocks are independent with each other. Unlike the setup in First-Generation Model (see references [18], [19] and [20]), here we assume that the attractiveness evolves over
(Ⅰ) Committing crimes.
A Type (Ⅰ) Poisson clock is assigned to each agent to govern his action of committing crimes like burgling. Suppose that a Type (Ⅰ) clock of an agent in district
For agent activity Type (Ⅰ) described as above, there is an alternative description. Suppose that each criminal has a Poisson clock with rate
(Ⅱ) Moving along districts.
A Type (Ⅱ) Poisson clock is assigned to each agent to govern his movement. Suppose that a Type (Ⅱ) clock of an agent advances at time
qK→J(t)=ˉAJ(t−)TK(t−), | (4) |
where
δt≅1c2L2. | (5) |
(Ⅲ) Replacement.
A Type (Ⅲ) Poisson clock is assigned to each district
Remark 1. The Poisson clock is particularly suitable to model random arrivals and has been used to build Poisson processes which are widely applied in biology, economics, and physics (for references see those cited in references [19] and [20]).
Following the setup of First-Generation Model (see references [18], [19] and [20]), we assume that the attractiveness field react to the agent activities described above according to the repeat and near-repeat victimization, and the broken-windows effects (see the related references in references [7], [18], [19] and [20]).
To model the repeat victimization and broken windows effects, we assume that the attractiveness increases continuously in time, with an increasing rate depending upon the average number of criminal events at the current location. However, this increase has a finite lifetime and decays at a certain speed. And we model the near-repeat victimization effect by letting the attractiveness to spread diffusively in space. Summing up, between the Poisson jumps of the criminal activities, the following partial different equation describes the spatio-temporal evolution of
dB(x,t)dt=c1θNK(t)A(x,t)−ωB(x,t)−ηΔB(x,t),x∈DK,K∈D. | (6) |
Here
For
N(x,t)=∑K∈DNK1K(t)(x), | (7) |
where
dA(t,x)dt=θc1N(x,t)A(x,t)−ωB(x,t)+ηΔB(x,t). | (8) |
We note that to completely specify the coupled dynamics of criminal activities and attractiveness field evolution, it is sufficient to keep track of the total number of criminals in each district. To conclude, the scales of the criminal activity and the evolution of the attractiveness field separate automatically.
We simulate M-IPC Model described above under various combinations of the parameters, in order to give insight into the behavior regime of the model, and compare it with the DTS, SSRB, and SSRB-IPC Models. The resulting attractiveness plots are displayed in Figs. 1 and 2.
Behavioral regimes similar to those in references [18], [19] and [20] for the attractiveness field
Case 1 Stationary hotspots. In this regime, the system tends towards an almost steady state in which stationary spots of high attractiveness are found, surrounded by areas of extremely low attractiveness.
Case 2 Dynamic hotspots. In this regime, loclized spots of increased attractiveness form and are transient. These spots remain for varying lengths of time, and may appear and disappear at seemingly random locations, and move about in space and deform over time. There are two sub categories, depending on whether the hotspots interact between each other or not.
Case 2.1. Independent hotspots. Hotspots each change in size and shape but any two hotspots rarely have interactions.
Case 2.2. Interactive hotspots. Hotspots repeatedly and constantly merge with each other and split. This regime reveals the intrinsic randomness of the model.
All the simulations were run with
In Fig. 1 (a), we set
The parameter regimes are robust for all three cases of hotspot dynamics mentioned above, and the same hotspot dynamics is also observed over other random paths with the same parameters used to create the plots in Fig. 1. In Fig. 2, distinct-stochastic-basis realizations for Fig. 1(c) are displayed.
Remark 2. The numerical scheme for (6) is similar to that in reference [18]. We use a semi-implicit scheme in which
[1+ωδt−ηδtΔ]Am+1=Am+θc1δtNmAm. | (9) |
Here
The coupled dynamics of
First introduced as in reference [4], a PDMP, roughly speaking, is a dynamic system occasionally interrupted by a pure jump process whose states then switch according to certain probability distributions (see e.g. references [2], [5], [8] and [10]).
We start with a standard stochastic basis
dU(t)dt=L(U(t)). | (10) |
Alternatively, starting from
P(τ1>t|U(0)=U0)=exp(−∫t0Λ(U(s))ds). | (11) |
Then
The infinitesimal generator of the stochastic process can be derived following e.g. references [4], [5], [8], [10] and [17] as
Tf(u)=Λ(u)[Qf(u)−∫Ef(u)Q(u;dv)]+L(u)∘∇f(u), | (12) |
for every
f(U(t))=f(U(0))+∫t0Tf(U(s))ds+M(t), | (13) |
where
For every
⟨N(t),ϕ⟩=∫MN(x,t)ϕ(x)dx=L−2∑K∈DNK(t)ˉϕK, | (14) |
where
ˉϕK:=∫DKϕ(x)dx. |
We define
⟨(N(t),B(t)),ϕ⟩:=(⟨N(t),ϕ⟩,⟨B(t),ϕ⟩). | (15) |
Then
Theorem 4.1. Starting with the initial data
{⟨N(t),ϕ⟩=⟨N0,ϕ⟩+∫t0Λ(N(s),B(s))(Q1−I1)⟨(N(s),B(s)),ϕ⟩ds+M1(⟨(N(t),B(t)),ϕ⟩),⟨B(t),ϕ⟩=⟨B0,ϕ⟩+∫t0L2(⟨(N(s),B(s)),ϕ⟩)ds+M2(⟨(N(t),B(t)),ϕ⟩), | (16) |
where
Λ(N(t),B(t))=c2L2∑K∈DNK(t)+L2c1∑K∈DNK(t)ˉAK(t)+L2Γ, | (17) |
(Q1−I1)⟨(N(t),B(t)),ϕ⟩=c2∑K∈DNK(t)(∑K′K′∼KˉAK′(t)TK(t)(ˉϕK′−ˉϕK))c2L2∑K∈DNK(t)+L2c1∑K∈DNK(t)ˉAK(t)+L2Γ−c1∑K∈DˉAK(t)NK(t)ˉϕKc2L2∑K∈DNK(t)+L2c1∑K∈DNK(t)ˉAK(t)+L2Γ+ΓL−2∑K∈DˉϕKc2L2∑K∈DNK(t)+L2c1∑K∈DNK(t)ˉAK(t)+L2Γ, | (18) |
L2(⟨(N(t),B(t)),ϕ⟩)=∫M[θc1N(x,t)A(x,t)−ωB(x,t)+ηΔB(x,t)]ϕ(x)dx. | (19) |
In other words, the deterministic flow of the PDMP driven by
Proof of Theorem 4.1. Substituting
Tu=Λ(u)[Qu−u∫EQ(u;dv)]+L(u)=Λ(u)(Q−I)u+L(u). | (20) |
Thus we have for the vector-valued stochastic process
T⟨(N(t),B(t)),ϕ⟩=(L1(⟨(N(t),B(t)),ϕ⟩),L2(⟨(N(t),B(t)),ϕ⟩))+Λ(N(t),B(t))((Q1−I1)⟨(N(t),B(t)),ϕ⟩,(Q2−I2)⟨(N(t),B(t)),ϕ⟩), | (21) |
where
In between advancements of Poisson clocks, the value of
L1(⟨(N(t),B(t)),ϕ⟩)=d⟨N(t),ϕ⟩dt=⟨dN(t)dt,ϕ⟩=0. | (22) |
Similarly as
L2(⟨(N(t),B(t)),ϕ⟩)=d⟨B(t),ϕ⟩dt=⟨dB(t)dt,ϕ⟩, | (23) |
which together with (6) implies (19).
Since all the Poisson clocks in the system are independent,
Suppose that a Poisson clock advances at time
Λ(N(t),B(t))(Q1−I1)⟨(N(t),B(t)),ϕ⟩=limδt→01δtE[⟨B(δt+t−),ϕ⟩−⟨B(t−),ϕ⟩|(N(t−),B(t−))]=L2c1∑K∈DˉAK(t)NK(t)(−L−2ˉϕK)+c2L2∑K∈D∑K′K′∼KˉAK′(t)TK(t)(L−2NK(t)(ˉϕK′−ˉϕK))+Γ∑K∈DL−2ˉϕK=−c1∑K∈DˉAK(t)NK(t)ˉϕK+c2∑K∈D∑K′K′∼KˉAK′(t)TK(t)NK(t)(ˉϕK′−ˉϕK)+L−2Γ∑K∈DˉϕK. | (24) |
This together with (17) implies (18). From (6), we infer that the flow
limδt→0[⟨B(δt+t−),ϕ⟩−⟨B(t−),ϕ⟩]=0, | (25) |
which implies that
Λ(N(t−),B(t−))(Q2−I2)⟨(N(t−),B(t−)),ϕ⟩=limδt→01δtE[⟨B(δt+t−),ϕ⟩−⟨B(t−),ϕ⟩|(N(t−),B(t−))]=Λ(N(t−),B(t−))×0=0. | (26) |
With every term on the right-hand-side of (21) derived, from (12), (13), (20), and (21) we obtain (16).
The proof of Theorem 4.1 is completed.
Remark 3. Unlike First-Generation Model, the finite size effects are circumvented here. Nevertheless, our model still generates interesting pattern formation of hotspot dynamics. Pattern formation in complex system has been studied extensively recently in natural and social sciences (see e.g. the references cited in the introduction of [20]). And the mathematical framework that we develop here based on PDMP theory and martingale formulation can be useful for quantitative study in this area, which has been lacking so far.
Ever since the pioneering agent-based residential burglary model of criminal behavior reference [18] is published, many works have been done aiming to study and improve the original model e.g. by incorporating random arrivals as in references [19] and [20]. In these models, residents are assumed to be located on the grids of a lattice field. Two components, the agent activities over the lattice and the level of attractiveness of target sites, are assumed to interact with each other on the same spatio-temporal scale. The continuum limit of First-Generation Model turns out to be reaction-advection-diffusion equations. However in the continuum simulations, transient hotspot regime is missing, which is not realistic as transient hotspots are well-document in empirical observations. A new generation of agent-based models of criminal behavior are needed.
In this work, we propose a multiscale model, where the attractiveness evolves continuously based on a diffusive partial different equation, whose parameter changes values whenever a Poisson clock associated with an agent action advances. Compared with First-Generation Model with two slow components, our model has a fast component and easier to simulate and analyze. Moreover, the stochastic nature of the model is successfully maintained and transient hotspot regimes with intrinsic randomness are picked up in the simulations as desired.
Furthermore, our multi-scale hybrid model can be characterized as a PDMP, which is applied in such a circumstance for the first time to the best of our knowledge. A martingale formula is derived, which consists of the deterministic component corresponding to the diffusive partial differential equation of attractiveness evolution, and the stochastic component corresponding to the Poisson arrivals. Results presented here will be transformative for both elements of application and analysis of agent-based models for criminal behavior.
We would like to thank the helpful discussions with Professors A. Debussche, A. L. Bertozzi, M. B. Short, T. Liggett, C. Mueller, J. Quastel, Da Kuang, Yifan Chen, Fangbo Zhang, Yatin Chow, Hangjie Ji, Yuanzhen Shao, and Kenneth Van. Yuan Zhang has been partially supported by Beijing Academy of Artificial Intelligence (BAAI).
[1] |
Fuso Nerini F, Sovacool B, Hughes N, et al. (2019) Connecting climate action with other Sustainable Development Goals. Nat Sustain 2: 674–680. https://doi.org/10.1038/s41893-019-0334-y. doi: 10.1038/s41893-019-0334-y
![]() |
[2] |
Lange-Salvia A, Leal Filho W, Londero Brandli L, et al. (2019) Assessing research trends related to Sustainable Development Goals: local and global issues. J Clean Prod 208: 841–849. https://doi.org/10.1016/j.jclepro.2018.09.242. doi: 10.1016/j.jclepro.2018.09.242
![]() |
[3] |
Galli A, Đurović G, Hanscom L, et al. (2018) Think globally, act locally: Implementing the sustainable development goals in Montenegro. Environ Sci Policy 84: 159– https://doi.org/169.10.1016/j.envsci.2018.03.012. doi: 10.1016/j.envsci.2018.03.012
![]() |
[4] |
Graute U (2015) Local Authorities Acting Globally for Sustainable Development. Reg Stud 50: 1931–1942. https://doi.org/10.1080/00343404.2016.1161740. doi: 10.1080/00343404.2016.1161740
![]() |
[5] |
Doukas H, Papadopoulou A, Savvakis N, et al. (2012) Assessing energy sustainability of rural communities using Principal Component Analysis. Renew Sustain Energy Rev 16: 1949–1957. http://dx.doi.org/10.1016/j.rser.2012.01.018. doi: 10.1016/j.rser.2012.01.018
![]() |
[6] |
Krakowiak-Bal A, Ziemianczyk U, Wozniak A, et al. (2017) Building entrepreneurial capacity in rural areas The use of AHP analysis for infrastructure evaluation. Int J Entrep Behav Res 23: 903–918. http://dx.doi.org/10.1108/IJEBR-07-2017-0223. doi: 10.1108/IJEBR-07-2017-0223
![]() |
[7] |
Marinakis V, Papadopoulou AG, Psarras J (2015) Local communities towards a sustainable energy future: needs and priorities. Int J Sustain Energy 36: 296–312. http://dx.doi.org/10.1080/14786451.2015.1018264. doi: 10.1080/14786451.2015.1018264
![]() |
[8] |
Abreu I, Nunes JM, Mesias FJ (2019) Can Rural Development Be Measured? Design and Application of a Synthetic Index to Portuguese Municipalities. Soc Indic Res 145: 1107–1123. https://doi.org/10.1007/s11205-019-02124-w. doi: 10.1007/s11205-019-02124-w
![]() |
[9] |
Dammers E, Keiner M (2006) Rural Development In Europe. disP - Plan Rev 42: 5– https://doi.org/15.10.1080/02513625.2006.10556958. doi: 10.1080/02513625.2006.10556958
![]() |
[10] |
Okkonen L, Lehtonen O (2016) Socio-economic impacts of community wind power projects in Northern Scotland. Renew Energy 85: 826–833. http://dx.doi.org/10.1016/j.renene.2015.07.047. doi: 10.1016/j.renene.2015.07.047
![]() |
[11] | Liu L, Cao C, Song W (2023) Bibliometric Analysis in the Field of Rural Revitalization: Current Status, Progress, and Prospects. Int J Environ Res Public Health 20. http://dx.doi.org/10.3390/ijerph20010823. |
[12] | de Los Ríos-Carmenado I, Ortuño M, Rivera M (2016) Private-Public Partnership as a tool to promote entrepreneurship for sustainable development: WWP torrearte experience. Sustainability 8. http://dx.doi.org/10.3390/su8030199. |
[13] |
Díaz-Cuevas P, Domínguez-Bravo J, Prieto-Campos A (2019) Integrating MCDM and GIS for renewable energy spatial models: assessing the individual and combined potential for wind, solar and biomass energy in Southern Spain. Clean Technol Environ Policy 21: 1855–1869. https://doi.org/10.1007/s10098-019-01754-5. doi: 10.1007/s10098-019-01754-5
![]() |
[14] |
Marinakis V, Papadopoulou AG, Psarras J (2017) Local communities towards a sustainable energy future: needs and priorities. Int J Sustain Energy 36: 296–312. http://dx.doi.org/10.1080/14786451.2015.1018264. doi: 10.1080/14786451.2015.1018264
![]() |
[15] | Streimikiene D, Baležentis T, Volkov A, et al. (2021) Barriers and drivers of renewable energy penetration in rural areas. Energies 14. http://dx.doi.org/10.3390/en14206452. |
[16] |
Reddy AKN (2002) A generic Southern perspective on renewable energy. Energy Sustain Dev 6: 74–83. http://dx.doi.org/10.1016/S0973-0826(08)60327-0. doi: 10.1016/S0973-0826(08)60327-0
![]() |
[17] |
Kitchen L, Marsden T (2009) Creating sustainable rural development through stimulating the eco-economy: Beyond the eco-economic paradox? Sociol Ruralis 49: 273–294. http://dx.doi.org/10.1111/j.1467-9523.2009.00489.x. doi: 10.1111/j.1467-9523.2009.00489.x
![]() |
[18] |
Graziano M, Billing SL, Kenter JO, et al. (2017) A transformational paradigm for marine renewable energy development. Energy Res Soc Sci 23: 136–147. http://dx.doi.org/10.1016/j.erss.2016.10.008. doi: 10.1016/j.erss.2016.10.008
![]() |
[19] |
Poggi F, Firmino A, Amado M (2018) Planning renewable energy in rural areas: Impacts on occupation and land use. Energy 155: 630–640. https://doi.org/10.1016/j.energy.2018.05.009. doi: 10.1016/j.energy.2018.05.009
![]() |
[20] |
Streimikiene D, Baležentis T, Kriščiukaitiene I (2012) Promoting interactions between local climate change mitigation, sustainable energy development, and rural development policies in Lithuania. Energy Policy 50: 699–710. https://doi.org/10.1016/j.enpol.2012.08.015. doi: 10.1016/j.enpol.2012.08.015
![]() |
[21] |
Brummer V (2018) Community energy – benefits and barriers: A comparative literature review of Community Energy in the UK, Germany and the USA, the benefits it provides for society and the barriers it faces. Renew Sustain Energy Rev 94: 187–196. https://doi.org/10.1016/j.rser.2018.06.013. doi: 10.1016/j.rser.2018.06.013
![]() |
[22] |
García-Martínez J, Reyes-Patiño JL, López-Sosa LB, et al. (2022) Anticipating alliances of stakeholders in the optimal design of community energy systems. Sustain Energy Technol Assessments 54: 102880. https://doi.org/10.1016/j.seta.2022.102880. doi: 10.1016/j.seta.2022.102880
![]() |
[23] |
Paredes-Sánchez JP, López-Ochoa LM, López-González LM, et al. (2018) Energy utilization for distributed thermal production in rural areas: A case study of a self-sustaining system in Spain. Energy Convers Manag 174: 1014–1023. https://doi.org/10.1016/j.enconman.2018.08.080. doi: 10.1016/j.enconman.2018.08.080
![]() |
[24] |
Van Hoesen J, Letendre S (2010) Evaluating potential renewable energy resources in Poultney, Vermont: A GIS-based approach to supporting rural community energy planning. Renew Energy 35: 2114–2122. http://dx.doi.org/10.1016/j.renene.2010.01.018. doi: 10.1016/j.renene.2010.01.018
![]() |
[25] |
Hain JJ, Ault GW, Galloway SJ, et al. (2005) Additional renewable energy growth through small-scale community orientated energy policies. Energy Policy 33: 1199–1212. http://dx.doi.org/10.1016/j.enpol.2003.11.017. doi: 10.1016/j.enpol.2003.11.017
![]() |
[26] |
Martire S, Tuomasjukka D, Lindner M, et al. (2015) Sustainability impact assessment for local energy supplies' development - The case of the alpine area of Lake Como, Italy. Biomass and Bioenergy 83: 60–76. http://dx.doi.org/10.1016/j.biombioe.2015.08.020. doi: 10.1016/j.biombioe.2015.08.020
![]() |
[27] |
Zabaniotou A, Rovas D, Delivand MK, et al. (2017) Conceptual vision of bioenergy sector development in Mediterranean regions based on decentralized thermochemical systems. Sustain Energy Technol Assessments 23: 33–47. http://dx.doi.org/10.1016/j.seta.2017.09.006. doi: 10.1016/j.seta.2017.09.006
![]() |
[28] |
von Bock und Polach C, Kunze C, Maaß O, et al. (2015) Bioenergy as a socio-technical system: The nexus of rules, social capital and cooperation in the development of bioenergy villages in Germany. Energy Res Soc Sci 6: 128–135. http://dx.doi.org/10.1016/j.erss.2015.02.003. doi: 10.1016/j.erss.2015.02.003
![]() |
[29] |
Klepacki B, Kusto B, Bórawski P, et al. (2021) Investments in renewable energy sources in basic units of local government in rural areas. Energies 14: 1–17. http://dx.doi.org/10.3390/en14113170. doi: 10.3390/en14113170
![]() |
[30] |
Wang Y, Cai C, Liu C, et al. (2022) Planning research on rural integrated energy system based on coupled utilization of biomass-solar energy resources. Sustain Energy Technol Assessments 53: 102416. https://doi.org/10.1016/j.seta.2022.102416. doi: 10.1016/j.seta.2022.102416
![]() |
[31] |
Poggi F, Firmino A, Amado M (2020) Shaping energy transition at municipal scale: A net-zero energy scenario-based approach. Land use policy 99: 104955. https://doi.org/10.1016/j.landusepol.2020.104955. doi: 10.1016/j.landusepol.2020.104955
![]() |
[32] |
Markantoni M, Woolvin M (2013) The role of rural communities in the transition to a low-carbon Scotland: A review. Local Environ 20: 202–219. http://dx.doi.org/10.1080/13549839.2013.834880. doi: 10.1080/13549839.2013.834880
![]() |
[33] | OECD (2012) Linking Renewable Energy to Rural Development. |
[34] | ECA (2018) Special Report No. 05. Renewable energy for sustainable rural development: significant potential synergies, but mostly unrealized., Luxembourg. |
[35] |
Clausen LT, Rudolph D (2020) Renewable energy for sustainable rural development: Synergies and mismatches. Energy Policy 138: 111289. https://doi.org/10.1016/j.enpol.2020.111289. doi: 10.1016/j.enpol.2020.111289
![]() |
[36] |
Katsaprakakis D Al, Christakis DG (2016) The exploitation of electricity production projects from Renewable Energy Sources for the social and economic development of remote communities. the case of Greece: An example to avoid. Renew Sustain Energy Rev 54: 341–349. http://dx.doi.org/10.1016/j.rser.2015.10.029. doi: 10.1016/j.rser.2015.10.029
![]() |
[37] |
O'Sullivan K, Golubchikov O, Mehmood A (2020) Uneven energy transitions: Understanding continued energy peripheralization in rural communities. Energy Policy 138: 111288. https://doi.org/10.1016/j.enpol.2020.111288. doi: 10.1016/j.enpol.2020.111288
![]() |
[38] |
Dütschke E, Wesche JP (2018) The energy transformation as a disruptive development at community level. Energy Res Soc Sci 37: 251–254. https://doi.org/10.1016/j.erss.2017.10.030. doi: 10.1016/j.erss.2017.10.030
![]() |
[39] |
Rommel J, Radtke J, von Jorck G, et al. (2018) Community renewable energy at a crossroads: A think piece on degrowth, technology, and the democratization of the German energy system. J Clean Prod 197: 1746–1753. https://doi.org/10.1016/j.jclepro.2016.11.114. doi: 10.1016/j.jclepro.2016.11.114
![]() |
[40] |
Sliz-Szkliniarz B (2013) Assessment of the renewable energy-mix and land use trade-off at a regional level: A case study for the Kujawsko-Pomorskie Voivodship. Land use policy 35: 257–270. http://dx.doi.org/10.1016/j.landusepol.2013.05.018. doi: 10.1016/j.landusepol.2013.05.018
![]() |
[41] |
Kumar N, Namrata K, Samadhiya A (2023) Techno socio-economic analysis and stratified assessment of hybrid renewable energy systems for electrification of rural community. Sustain Energy Technol Assessments 55: 102950. https://doi.org/10.1016/j.seta.2022.102950. doi: 10.1016/j.seta.2022.102950
![]() |
[42] |
Ma W, Xue X, Liu G (2018) Techno-economic evaluation for hybrid renewable energy system: Application and merits. Energy 159: 385–409. https://doi.org/10.1016/j.energy.2018.06.101. doi: 10.1016/j.energy.2018.06.101
![]() |
[43] |
He J, Wu Y, Wu J, et al. (2021) Towards cleaner heating production in rural areas: Identifying optimal regional renewable systems with a case in Ningxia, China. Sustain Cities Soc 75: 103288. https://doi.org/10.1016/j.scs.2021.103288. doi: 10.1016/j.scs.2021.103288
![]() |
[44] |
Li S, Zhang L, Wang X, et al. (2022) A decision-making and planning optimization framework for multi-regional rural hybrid renewable energy system. Energy Convers Manag 273: 116402. https://doi.org/10.1016/j.enconman.2022.116402. doi: 10.1016/j.enconman.2022.116402
![]() |
[45] |
Hori K, Matsui T, Hasuike T, et al. (2016) Development and application of the renewable energy regional optimization utility tool for environmental sustainability: REROUTES. Renew Energy 93: 548–561. http://dx.doi.org/10.1016/j.renene.2016.02.051. doi: 10.1016/j.renene.2016.02.051
![]() |
[46] |
Woch F, Hernik J, Linke HJ, et al. (2017) Renewable energy and rural autonomy: A case study with generalizations. Polish J Environ Stud 26: 2823–2832. http://dx.doi.org/10.15244/pjoes/74129. doi: 10.15244/pjoes/74129
![]() |
[47] |
Romero-Castro N, Miramontes-Viña V, López-Cabarcos MÁ (2022) Understanding the Antecedents of Entrepreneurship and Renewable Energies to Promote the Development of Community Renewable Energy in Rural Areas. Sustain 14: 1–25. http://dx.doi.org/10.3390/su14031234. doi: 10.3390/su14031234
![]() |
[48] | Romero-Castro N, Ángeles López-Cabarcos M, Miramontes-Viña V, et al. (2023) Sustainable energy transition and circular economy: The heterogeneity of potential investors in rural community renewable energy projects. Environ Dev Sustain. https://doi.org/10.1007/s10668-022-02898-z. |
[49] |
D'Souza C, Yiridoe EK (2014) Social acceptance of wind energy development and planning in rural communities of Australia: A consumer analysis. Energy Policy 74: 262–270. http://dx.doi.org/10.1016/j.enpol.2014.08.035. doi: 10.1016/j.enpol.2014.08.035
![]() |
[50] |
Süsser D, Kannen A (2017) Renewables? Yes, please!': perceptions and assessment of community transition induced by renewable-energy projects in North Frisia. Sustain Sci 12: 563–578. http://dx.doi.org/10.1007/s11625-017-0433-5. doi: 10.1007/s11625-017-0433-5
![]() |
[51] |
Monteleone M, Cammerino ARB, Libutti A (2018) Agricultural "greening" and cropland diversification trends: Potential contribution of agroenergy crops in Capitanata (South Italy). Land use policy 70: 591–600. https://doi.org/10.1016/j.landusepol.2017.10.038. doi: 10.1016/j.landusepol.2017.10.038
![]() |
[52] | Sæ tórsdóttir AD, Hall CM (2019) Contested development paths and rural communities: Sustainable energy or sustainable tourism in Iceland? Sustain 11. https://doi.org/10.3390/su11133642. |
[53] |
Yildiz Ö (2014) Financing renewable energy infrastructures via financial citizen participation - The case of Germany. Renew Energy 68: 677–685. http://dx.doi.org/10.1016/j.renene.2014.02.038. doi: 10.1016/j.renene.2014.02.038
![]() |
[54] | Lowitzsch J, Hanke F (2019) Energy transition: Financing consumer co-ownership in renewables. Energy Transit Financ Consum Co-ownersh Renewables 139–162. http://dx.doi.org/10.1007/978-3-319-93518-8. |
[55] | Schreuer A, Weismeier-Sammer D (2010) Energy cooperatives and local Ownership in the field of renewable energy technologies: A literature review. |
[56] |
McKenna R (2018) The double-edged sword of decentralized energy autonomy. Energy Policy 113: 747–750. https://doi.org/10.1016/j.enpol.2017.11.033. doi: 10.1016/j.enpol.2017.11.033
![]() |
[57] |
Lam PTI, Law AOK (2016) Crowdfunding for renewable and sustainable energy projects: An exploratory case study approach. Renew Sustain Energy Rev 60: 11–20. http://dx.doi.org/10.1016/j.rser.2016.01.046. doi: 10.1016/j.rser.2016.01.046
![]() |
[58] |
Martínez-Alonso P, Hewitt R, Pacheco JD, et al. (2016) Losing the roadmap: Renewable energy paralysis in Spain and its implications for the EU low carbon economy. Renew Energy 89: 680–694. http://dx.doi.org/10.1016/j.renene.2015.12.004. doi: 10.1016/j.renene.2015.12.004
![]() |
[59] |
Ryberg DS, Robinius M, Stolten D (2018) Evaluating land eligibility constraints of renewable energy sources in Europe. Energies 11: 1–19. http://dx.doi.org/10.3390/en11051246. doi: 10.3390/en11051246
![]() |
[60] |
Medina-Santana AA, Flores-Tlacuahuac A, Cárdenas-Barrón LE, et al. (2020) Optimal design of the water-energy-food nexus for rural communities. Comput Chem Eng 143: 107120. https://doi.org/10.1016/j.compchemeng.2020.107120. doi: 10.1016/j.compchemeng.2020.107120
![]() |
[61] |
Singh A, Yadav A, Sinha S (2022) Hybrid Power Systems: Solution to Rural Electrification. Curr Sustain Energy Reports 9: 77–93. https://doi.org/10.1007/s40518-022-00206-x. doi: 10.1007/s40518-022-00206-x
![]() |
[62] |
Zhang G, Shi Y, Maleki A, et al. (2020) Optimal location and size of a grid-independent solar/hydrogen system for rural areas using an efficient heuristic approach. Renew Energy 156: 1203–1214. https://doi.org/10.1016/j.renene.2020.04.010. doi: 10.1016/j.renene.2020.04.010
![]() |
[63] |
Elkadeem MR, Younes A, Sharshir SW, et al. (2021) Sustainable siting and design optimization of hybrid renewable energy system: A geospatial multi-criteria analysis. Appl Energy 295: 117071. https://doi.org/10.1016/j.apenergy.2021.117071. doi: 10.1016/j.apenergy.2021.117071
![]() |
[64] |
Izadyar N, Ong HC, Chong WT, et al. (2016) Investigation of potential hybrid renewable energy at various rural areas in Malaysia. J Clean Prod 139: 61–73. http://dx.doi.org/10.1016/j.jclepro.2016.07.167. doi: 10.1016/j.jclepro.2016.07.167
![]() |
[65] |
Angelis-Dimakis A, Biberacher M, Dominguez J, et al. (2011) Methods and tools to evaluate the availability of renewable energy sources. Renew Sustain Energy Rev 15: 1182– http://dx.doi.org/1200.10.1016/j.rser.2010.09.049. doi: 10.1016/j.rser.2010.09.049
![]() |
[66] |
Šúri M, Huld TA, Dunlop ED, et al. (2007) Potential of solar electricity generation in the European Union member states and candidate countries. Sol Energy 81: 1295– http://dx.doi.org/1305.10.1016/j.solener.2006.12.007. doi: 10.1016/j.solener.2006.12.007
![]() |
[67] |
Barragán-Escandón E, Zalamea-León E, Terrados-Cepeda J, et al. (2019) Factores que influyen en la selección de energías renovables en la ciudad. Eure 45: 259–277. http://dx.doi.org/10.4067/S0250-71612019000100259. doi: 10.4067/S0250-71612019000100259
![]() |
[68] | Potrč S, Čuček L, Martin M, et al. (2021) Sustainable renewable energy supply networks optimization – The gradual transition to a renewable energy system within the European Union by 2050. Renew Sustain Energy Rev 146. http://dx.doi.org/10.1016/j.rser.2021.111186. |
[69] |
Roberts JJ, Cassula AM, Osvaldo Prado P, et al. (2015) Assessment of dry residual biomass potential for use as alternative energy source in the party of General Pueyrredón, Argentina. Renew Sustain Energy Rev 41: 568–583. https://doi.org/10.1016/j.rser.2014.08.066. doi: 10.1016/j.rser.2014.08.066
![]() |
[70] |
Fridleifsson IB (2001) Geothermal energy for the benefit of the people. Renew Sustain Energy Rev 5: 299–312. https://doi.org/10.1016/S1364-0321(01)00002-8. doi: 10.1016/S1364-0321(01)00002-8
![]() |
[71] |
Hurter S, Schellschmidt R (2003) Atlas of geothermal resources in Europe. Geothermics 32: 779–787. https://doi.org/10.1016/S0375-6505(03)00070-1. doi: 10.1016/S0375-6505(03)00070-1
![]() |
[72] | EUROPEAN SMALL HYDROPOWER ASSOCIATION (2006) Guía para el desarrollo de una pequeña central hidroeléctrica, Bruselas. |
[73] | Espejo Marín C, García Marín R, Aparicio Guerrero AE (2016) La energía minihidráulica en los planes de fomento de las energías renovables en España, Paisaje, cultura territorial y vivencia de la geografía: Libro homenaje al profesor Alfredo Morales Gil, 507–533. |
[74] | IDAE (2006) Minicentrales Hidroeléctricas, Madrid. |
[75] | Espejo Marín C, García Marín R, Aparicio Guerrero AE (2017) El resurgimiento de la energía minihidráulica en España y su situación actual 1. Rev Geogr Norte Gd 67: 115–143. |
[76] |
Palla A, Gnecco I, La Barbera P, et al. (2016) An Integrated GIS Approach to Assess the Mini Hydropower Potential. Water Resour Manag 30: 2979–2996. https://doi.org/10.1007/s11269-016-1318-6. doi: 10.1007/s11269-016-1318-6
![]() |
[77] |
Bergmann A, Colombo S, Hanley N (2008) Rural versus urban preferences for renewable energy developments. Ecol Econ 65: 616–625. https://doi.org/10.1016/j.ecolecon.2007.08.011. doi: 10.1016/j.ecolecon.2007.08.011
![]() |
[78] |
Kalkbrenner BJ, Roosen J (2016) Citizens' willingness to participate in local renewable energy projects: The role of community and trust in Germany. Energy Res Soc Sci 13: 60–70. http://dx.doi.org/10.1016/j.erss.2015.12.006. doi: 10.1016/j.erss.2015.12.006
![]() |
[79] |
Wang J-J, Jing Y-Y, Zhang C-F, et al. (2009) Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew Sustain Energy Rev 13: 2263–2278. http://doi.org/10.1016/j.enpol.2013.09.059. doi: 10.1016/j.enpol.2013.09.059
![]() |
[80] |
Boon FP, Dieperink C (2014) Local civil society based renewable energy organisations in the Netherlands: Exploring the factors that stimulate their emergence and development. Energy Policy 69: 297–307. http://doi.org/10.1016/j.enpol.2014.01.046. doi: 10.1016/j.enpol.2014.01.046
![]() |
[81] |
Loomis DG, Hayden J, Noll S, et al. (2016) Economic Impact of Wind Energy in Illinois. J Bus Valuat Econ Loss Anal 11: 3–23. http://doi.org/10.1515/jbvela-2015-0008. doi: 10.1515/jbvela-2015-0008
![]() |
[82] | Bere J, Jones C, Jones S, et al. (2017) Energy and development in the periphery: A regional perspective on small hydropower projects. Environ Plan C Polit Sp 35: 355–375. http://journals.sagepub.com/doi/10.1177/0263774X16662029. |
[83] |
Bauwens T (2016) Explaining the diversity of motivations behind community renewable energy. Energy Policy 93: 278–290. http://dx.doi.org/10.1016/j.enpol.2016.03.017. doi: 10.1016/j.enpol.2016.03.017
![]() |
[84] |
Dóci G, Vasileiadou E (2015) 'Let's do it ourselves' Individual motivations for investing in renewables at community level. Renew Sustain Energy Rev 49: 41–50. http://doi.org/10.1016/j.rser.2015.04.051. doi: 10.1016/j.rser.2015.04.051
![]() |
[85] | Helming K, Pérez-Soba M (2011) Landscape Scenarios and Multifunctionality : Making Land Use Impact. Ecol Soc 16 http://www.ecologyandsociety.org/vol16/iss1/art50/ES-2011-4042.pdf. |
[86] |
Wiggering H, Dalchow C, Glemnitz M, et al. (2006) Indicators for multifunctional land use - Linking socio-economic requirements with landscape potentials. Ecol Indic 6: 238–249. https://doi.org/10.1016/j.ecolind.2005.08.014. doi: 10.1016/j.ecolind.2005.08.014
![]() |
[87] |
Krewitt W, Nitsch J (2003) The potential for electricity generation from on-shore wind energy under the constraints of nature conservation: A case study for two regions in Germany. Renew Energy 28: 1645–1655. https://doi.org/10.1016/S0960-1481(03)00008-9. doi: 10.1016/S0960-1481(03)00008-9
![]() |
[88] |
Chiabrando R, Fabrizio E, Garnero G (2009) The territorial and landscape impacts of photovoltaic systems: Definition of impacts and assessment of the glare risk. Renew Sustain Energy Rev 13: 2441–2451. https://doi.org/10.1016/j.rser.2009.06.008. doi: 10.1016/j.rser.2009.06.008
![]() |
[89] |
Tsoutsos T, Frantzeskaki N, Gekas V (2005) Environmental impacts from the solar energy technologies. Energy Policy 33: 289–296. https://doi.org/10.1016/S0301-4215(03)00241-6. doi: 10.1016/S0301-4215(03)00241-6
![]() |
[90] |
Dijkman TJ, Benders RMJ (2010) Comparison of renewable fuels based on their land use using energy densities. Renew Sustain Energy Rev 14: 3148–3155. http://dx.doi.org/10.1016/j.rser.2010.07.029. doi: 10.1016/j.rser.2010.07.029
![]() |
[91] |
Russi D (2008) An integrated assessment of a large-scale biodiesel production in Italy: Killing several birds with one stone? Energy Policy 36: 1169–1180. https://doi.org/10.1016/j.enpol.2007.11.016. doi: 10.1016/j.enpol.2007.11.016
![]() |
[92] |
Huston MA, Marland G (2003) Carbon management and biodiversity. J Environ Manage 67: 77–86. https://doi.org/10.1016/S0301-4797(02)00190-1. doi: 10.1016/S0301-4797(02)00190-1
![]() |
[93] |
Robertson GP, Dale VH, Doering OC, et al. (2008) Agriculture: Sustainable biofuels redux. Science (80-) 322: 49–50. https://doi.org/10.1126/science.1161525. doi: 10.1126/science.1161525
![]() |
[94] |
Janhunen S, Hujala M, Pätäri S (2014) Owners of second homes, locals and their attitudes towards future rural wind farm. Energy Policy 73: 450–460. http://dx.doi.org/10.1016/j.enpol.2014.05.050. doi: 10.1016/j.enpol.2014.05.050
![]() |
[95] |
Paz Espinosa M, Pizarro-Irizar C (2018) Is renewable energy a cost-effective mitigation resource? An application to the Spanish electricity market. Renew Sustain Energy Rev 94: 902–914. https://doi.org/10.1016/j.rser.2018.06.065. doi: 10.1016/j.rser.2018.06.065
![]() |
[96] |
Capellán-Pérez I, Campos-Celador Á, Terés-Zubiaga J (2018) Renewable Energy Cooperatives as an instrument towards the energy transition in Spain. Energy Policy 123: 215–229. https://doi.org/10.1016/j.enpol.2018.08.064. doi: 10.1016/j.enpol.2018.08.064
![]() |
[97] | Campos I, Pontes Luz G, Marín González E, et al. (2020) Regulatory challenges and opportunities for collective renewable energy prosumers in the EU. Energy Policy 138. https://doi.org/10.1016/j.enpol.2019.111212. |
[98] | Frieden D, Roberts J, Gubina AF (2019) Overview of emerging regulatory frameworks on collective self-consumption and energy communities in Europe. Int Conf Eur Energy Mark EEM 2019-Septe: 1–6. https://doi.org/10.1109/EEM.2019.8916222. |
[99] |
Cuesta-Fernandez I, Belda-Miquel S, Calabuig Tormo C (2020) Challengers in energy transitions beyond renewable energy cooperatives: community-owned electricity distribution cooperatives in Spain. Innov Eur J Soc Sci Res 0: 1–20. https://doi.org/10.1080/13511610.2020.1732197. doi: 10.1080/13511610.2020.1732197
![]() |
[100] |
Heras-Saizarbitoria I, Sáez L, Allur E, et al. (2018) The emergence of renewable energy cooperatives in Spain: A review. Renew Sustain Energy Rev 94: 1036–1043. https://doi.org/10.1016/j.rser.2018.06.049- doi: 10.1016/j.rser.2018.06.049-
![]() |
[101] |
Romero-Rubio C, de Andrés Díaz JR (2015) Sustainable energy communities: A study contrasting Spain and Germany. Energy Policy 85: 397–409. http://dx.doi.org/10.1016/j.enpol.2015.06.012. doi: 10.1016/j.enpol.2015.06.012
![]() |
[102] | Burgueño J, Lladós MG (2014) The municipal map of Spain: A geographical description. Bol la Asoc Geogr Esp 407–414. |
[103] |
Delgado Viñas C (2019) Depopulation processes in European Rural Areas: A case study of Cantabria (Spain). Eur Countrys 11: 341–369. http://dx.doi.org/10.2478/euco-2019-0021. doi: 10.2478/euco-2019-0021
![]() |
[104] |
Martínez-Filgueira X, Peón D, López-Iglesias E (2017) Intra-rural divides and regional planning: an analysis of a traditional emigration region (Galicia, Spain). Eur Plan Stud 25: 1237–1255. http://dx.doi.org/10.1080/09654313.2017.1319465 doi: 10.1080/09654313.2017.1319465
![]() |
[105] |
López-Iglesias E, Peón D, Rodríguez-Álvarez J (2018) Mobility innovations for sustainability and cohesion of rural areas: A transport model and public investment analysis for Valdeorras (Galicia, Spain). J Clean Prod 172: 3520–3534. https://doi.org/10.1016/j.jclepro.2017.05.149. doi: 10.1016/j.jclepro.2017.05.149
![]() |
[106] |
Pose DP, Martínez-Filgueira XM, López-Iglesias E (2020) Productive vs. Residential economy: Factors behind the recovery of rural areas in socioeconomic decline. Rev Galega Econ 29: 1–30. https://doi.org/10.15304/rge.29.2.6744. doi: 10.15304/rge.29.2.6744
![]() |
[107] |
Copena D, Simón X (2018) Wind farms and payments to landowners: Opportunities for rural development for the case of Galicia. Renew Sustain Energy Rev 95: 38–47. https://doi.org/10.1016/j.rser.2018.06.043. doi: 10.1016/j.rser.2018.06.043
![]() |
[108] |
Simón X, Copena D, Montero M (2019) Strong wind development with no community participation. The case of Galicia (1995–2009). Energy Policy 133: 110930. https://doi.org/10.1016/j.enpol.2019.110930. doi: 10.1016/j.enpol.2019.110930
![]() |
[109] |
Montoya FG, Aguilera MJ, Manzano-Agugliaro F (2014) Renewable energy production in Spain: A review. Renew Sustain Energy Rev 33: 509–531. https://doi.org/10.1016/j.rser.2014.01.091. doi: 10.1016/j.rser.2014.01.091
![]() |
[110] | Instituto Enerxético de Galicia (2020) Avance do Balance Enerxético de Galicia 2018. |
[111] | Copena Rodríguez D, Simón Fernández X (2018) Enerxía eólica e desenvolvemento local en galicia: os parques eólicos singulares municipais. Rev Galega Econ 27: 31–48. |
[112] | Maimó-Far A, Tantet A, Homar V, et al. (2020) Predictable and unpredictable climate variability impacts on optimal renewable energy mixes: The example of Spain. Energies 13. https://doi.org/10.3390/en13195132. |
[113] | Gregorio M De (2020) Biomasa en España. Generación de valor añadido y análisis prospectivo. |
[114] |
Benedek J, Sebestyén TT, Bartók B (2018) Evaluation of renewable energy sources in peripheral areas and renewable energy-based rural development. Renew Sustain Energy Rev 90: 516–535. https://doi.org/10.1016/j.rser.2018.03.020. doi: 10.1016/j.rser.2018.03.020
![]() |
[115] |
Igliński B, Buczkowski R, Cichosz M (2015) Biogas production in Poland - Current state, potential and perspectives. Renew Sustain Energy Rev 50: 686–695. https://doi.org/10.1016/j.rser.2015.05.013. doi: 10.1016/j.rser.2015.05.013
![]() |
[116] |
Corcoran; L, Coughlan; P, McNabola A (2013) Energy recovery potential using micro hydropower in water supply networks in the UK and Ireland. Water Supply 13: 552–560. https://doi.org/10.2166/ws.2013.050. doi: 10.2166/ws.2013.050
![]() |
[117] |
Langer K, Decker T, Roosen J, et al. (2018) Factors influencing citizens' acceptance and non-acceptance of wind energy in Germany. J Clean Prod 175: 133–144. https://doi.org/10.1016/j.jclepro.2017.11.221. doi: 10.1016/j.jclepro.2017.11.221
![]() |
[118] |
Colmenar-Santos A, Folch-Calvo M, Rosales-Asensio E, et al. (2016) The geothermal potential in Spain. Renew Sustain Energy Rev 56: 865–886. http://dx.doi.org/10.1016/j.rser.2015.11.070. doi: 10.1016/j.rser.2015.11.070
![]() |
[119] |
Østergaard PA, Mathiesen BV, Möller B, et al. (2010) A renewable energy scenario for Aalborg Municipality based on low-temperature geothermal heat, wind power and biomass. Energy 35: 4892–4901. http://dx.doi.org/10.1016/j.energy.2010.08.041. doi: 10.1016/j.energy.2010.08.041
![]() |
[120] |
Gan X, Fernandez IC, Guo J, et al. (2017) When to use what: Methods for weighting and aggregating sustainability indicators. Ecol Indic 81: 491–502. http://dx.doi.org/10.1016/j.ecolind.2017.05.068. doi: 10.1016/j.ecolind.2017.05.068
![]() |
[121] |
Li T, Zhang H, Yuan C, et al. (2012) A PCA-based method for construction of composite sustainability indicators. Int J Life Cycle Assess 17: 593–603. http://dx.doi.org/10.1007/s11367-012-0394-y. doi: 10.1007/s11367-012-0394-y
![]() |
[122] |
Salvati L, Carlucci M (2014) A composite index of sustainable development at the local scale: Italy as a case study. Ecol Indic 43: 162–171. http://dx.doi.org/10.1016/j.ecolind.2014.02.021. doi: 10.1016/j.ecolind.2014.02.021
![]() |
[123] |
Kotzee I, Reyers B (2016) Piloting a social-ecological index for measuring flood resilience: A composite index approach. Ecol Indic 60: 45–53. http://dx.doi.org/10.1016/j.ecolind.2015.06.018. doi: 10.1016/j.ecolind.2015.06.018
![]() |
[124] | Schlossarek M, Syrovátka M, Vencálek O (2019) The Importance of Variables in Composite Indices: A Contribution to the Methodology and Application to Development Indices, Springer Netherlands. |
[125] | OECD (2008) Handbook on constructing composite indicators: methodology and user guide. |
[126] |
Greco S, Ishizaka A, Tasiou M, et al. (2019) On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Soc Indic Res 141: 61–94. https://doi.org/10.1007/s11205-017-1832-9. doi: 10.1007/s11205-017-1832-9
![]() |
[127] | Pearson K (1901) LⅢ. On lines and planes of closest fit to systems of points in space. London, Edinburgh, Dublin Philos Mag J Sci 2: 559–572. |
[128] | Jolliffe IT (1986) Principal component analysis., New York, Springer. |
[129] | Jollife IT (2002) Principal Component Analysis, New York, Springer - Verlang. |
[130] |
Li Y, Shi X, Yao L (2016) Evaluating energy security of resource-poor economies: A modified principle component analysis approach. Energy Econ 58: 211–221. http://dx.doi.org/10.1016/j.eneco.2016.07.001. doi: 10.1016/j.eneco.2016.07.001
![]() |
[131] |
de Freitas DS, de Oliveira TE, de Oliveira JM (2019) Sustainability in the Brazilian pampa biome: A composite index to integrate beef production, social equity, and ecosystem conservation. Ecol Indic 98: 317–326. https://doi.org/10.1016/j.ecolind.2018.10.012. doi: 10.1016/j.ecolind.2018.10.012
![]() |
[132] |
González-García S, Rama M, Cortés A, et al. (2019) Embedding environmental, economic and social indicators in the evaluation of the sustainability of the municipalities of Galicia (northwest of Spain). J Clean Prod 234: 27–42. https://doi.org/10.1016/j.jclepro.2019.06.158. doi: 10.1016/j.jclepro.2019.06.158
![]() |
[133] |
Nogués S, González-González E, Cordera R (2019) Planning regional sustainability: An index-based framework to assess spatial plans. Application to the region of Cantabria (Spain). J Clean Prod 225: 510–523.https://doi.org/10.1016/j.jclepro.2019.03.328. doi: 10.1016/j.jclepro.2019.03.328
![]() |
[134] | Pontarollo N, Serpieri C (2018) A composite policy tool to measure territorial resilience capacity. Socioecon Plann Sci 100669. https://doi.org/10.1016/j.seps.2018.11.006. |
[135] |
Tapia C, Abajo B, Feliu E, et al. (2017) Profiling urban vulnerabilities to climate change: An indicator-based vulnerability assessment for European cities. Ecol Indic 78: 142–155. https://doi.org/10.1016/j.ecolind.2017.02.040. doi: 10.1016/j.ecolind.2017.02.040
![]() |
[136] | Lévy Mangin JP, Varela Mallou J (2003) Análisis Multivariante para las Ciencias Sociales, España. |
[137] | López-Roldán P, Fachelli S (2016) Parte Ⅲ. Análisis. Capítulo Ⅲ. 11. Análisis Factorial. Metodol la Investig Soc cuantitativa 140. |
[138] | Nardo M, Saisana M, Tarantola A, et al. (2005) Tools for Composite Indicators Building. 1–134. http://collection.europarchive.org/dnb/20070702132253/http://farmweb.jrc.ec.europa.eu/ci/Document/EUR 21682 EN.pdf. |
[139] |
Stockdale A (2006) Migration: Pre-requisite for rural economic regeneration? J Rural Stud 22: 354–366. https://doi.org/10.1016/j.jrurstud.2005.11.001. doi: 10.1016/j.jrurstud.2005.11.001
![]() |
[140] | Borch J, Odd A, Førde L, et al. (2008) Resource Configuration and Creative Practices of Community Entrepreneurs. J Enterprising Communities People Places Glob Econ 2. https://doi.org/10.1108/17506200810879943. |
[141] |
Baumgartner D, Schulz T, Seidl I (2013) Quantifying entrepreneurship and its impact on local economic performance: A spatial assessment in rural Switzerland. Entrep Reg Dev 25: 222–250. https://doi.org/10.1080/08985626.2012.710266. doi: 10.1080/08985626.2012.710266
![]() |
[142] |
Hussain A, Arif SM, Aslam M (2017) Emerging renewable and sustainable energy technologies: State of the art. Renew Sustain Energy Rev 71: 12–28. https://doi.org/10.1016/j.rser.2016.12.033 doi: 10.1016/j.rser.2016.12.033
![]() |
[143] |
Gormally AM, Whyatt JD, Timmis RJ, et al. (2012) A regional-scale assessment of local renewable energy resources in Cumbria, UK. Energy Policy 50: 283–293. http://dx.doi.org/10.1016/j.enpol.2012.07.015. doi: 10.1016/j.enpol.2012.07.015
![]() |
[144] |
Mainali B, Silveira S (2015) Using a sustainability index to assess energy technologies for rural electrification. Renew Sustain Energy Rev 41: 1351–1365. http://dx.doi.org/10.1016/j.rser.2014.09.018. doi: 10.1016/j.rser.2014.09.018
![]() |
[145] |
Slee B (2015) Is there a case for community-based equity participation in Scottish on-shore wind energy production? Gaps in evidence and research needs. Renew Sustain Energy Rev 41: 540–549. http://dx.doi.org/10.1016/j.rser.2014.08.064. doi: 10.1016/j.rser.2014.08.064
![]() |
[146] |
Berka AL, Creamer E (2018) Taking stock of the local impacts of community owned renewable energy: A review and research agenda. Renew Sustain Energy Rev 82: 3400–3419. https://doi.org/10.1016/j.rser.2017.10.050. doi: 10.1016/j.rser.2017.10.050
![]() |
![]() |
![]() |