Resources, Conservation & Recycling 209 (2024) 107808

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Resources, Conservation & Recycling
journal homepage: www.sciencedirect.com/journal/resources-conservation-and-recycling

Potential of agrivoltaics in ASEAN considering a scenario whereagroforestry expansion is also pursued
Brian A. Johnson a, * , Yosuke Arino a, c , Damasa B. Magcale-Macandog b , Xianbing Liu a ,Makino Yamanoshita aabcInstitute for Global Environmental Strategies, Hayama, Kanagawa 240-0115, Japan
Institute of Biological Sciences, University of the Philippines Los Baños, Laguna 4031, Philippines
Faculty of the Environment and Information Studies, Keio University, 5322 Endo, Fujisawa-shi, Kanagawa 252-0882 JapanA R T I C L E I N F O

A B S T R A C T

Keywords:
Solar power
Renewable energy
Ecosystem services
Nature-based solutions

Integration of solar photovoltaics on croplands (“agrivoltaics”) has been promoted as an environmentallyfriendly approach for solar energy deployment. Past studies, however, have not considered that these croplands could alternatively be used as sites for expanding agroforestry, a practice which provides important
ecosystem services to the neighbouring environment. We assessed the potential of agrivoltaics on herbaceous
croplands in ASEAN, considering potential trade-offs with agroforestry. We assumed that croplands located inenvironmentally sensitive areas (ESAs) – including protected areas, key biodiversity areas, forests, wetlands/
inland water bodies, their buffer zones, and areas with steep slopes – were better suited for agroforestry thanagrivoltaics. We found that even if agrivoltaics are prohibited on all croplands located in ESAs, using just 10 % of
the remaining land for agrivoltaics can still allow it to provide most of ASEAN’s electricity generation needs.
Thus, large-scale expansion of agrivoltaics need not conflict with regional efforts to enhance biodiversity/
ecosystem services through agroforestry.

1. Introduction
Solar photovoltaics (PV) has become one of the most promising
renewable energy technologies due to its increasing economic attractiveness, and recent projections show that it may generate more thanhalf of the world’s electricity by 2050 (Nijsse et al., 2023). Deploying
solar PV at such a scale would require a large amount of land, however,causing concerns that solar PV expansion might result in significant lossof natural/semi-natural ecosystems if unabated land-use conversion
occurs (Hernandez et al., 2015; Zhang et al., 2023). As an alternative toconverting land from other uses to build monofunctional solar power
plants, another option is to integrate solar PV into the existing land uses.
In particular, integrating solar PV panels in agricultural areas (i.e.,“agrivoltaics” (Dupraz et al., 2011)) can potentially provide a large
share of global energy demand due to the massive extent of croplands
globally and their typically high solar irradiation (Adeh et al., 2019).
Agrivoltaics typically involves mounting solar panels above or between
rows of crops or pasture (Fig. 1), and recent estimates indicate thatconverting less than 1 % of croplands worldwide to agrivoltaics could

theoretically offset the entire global energy demand (Adeh et al., 2019).
1.1. Overview of benefits and trade-offs of agrivoltaics
Previous studies have identified many potential benefits of agrivoltaics, with an obvious one being the ability to increase land use efficiency by allowing for crop cultivation and electricity generation
within the same land area (Dupraz et al., 2011). Agrivoltaics can also
help to diversify and increase farmers’ incomes (Chae et al., 2022;
Wagner et al., 2024) and reduce water consumption (Widmer et al.,2024). Because shading beneath solar panels can affect crop growth,
however, much research has focused on analysing the relationships
between crop yields and agrivoltaics design to optimize the selection ofcrops and the layout of solar panels (e.g., their height, ground coverratio, and solar tracking capabilities) (Ali Khan Niazi and Victoria, 2023;
Toledo and Scognamiglio, 2021; Yeligeti et al., 2023). These past studies
found that shade-loving crops (e.g., berries and lettuces) may have evenhigher yields when partially shaded by solar panels, as compared toopen field conditions (Laub et al., 2022; Marrou et al., 2013; Yeligeti* Corresponding author.
E-mail address: johnson@iges.or.jp (B.A. Johnson).
https://doi.org/10.1016/j.resconrec.2024.107808
Received 10 April 2024; Received in revised form 10 June 2024; Accepted 5 July 2024
Available online 11 July 2024
0921-3449/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Resources, Conservation & Recycling 209 (2024) 107808

trees/shrubs and crops, and can help maintain soil quality and favourable microclimate conditions throughout a farm (Fahmi et al., 2018)(Fig. A1(b)). Windbreak agroforestry systems involve planting trees/shrubs in rows alongside crops to reduce wind damage, while riparianbuffer agroforestry systems involve planting trees/shrubs intermixed
with crops along water bodies to, e.g., reduce bank erosion (Fig. A1(c))(Nerlich et al., 2013; Prastiyo et al., 2020). Various other types ofagroforestry systems exist, and have been highlighted in different reviews (Kuyah et al., 2019; Nerlich et al., 2013; Rodenburg et al., 2022).
Environmentally-friendly agroforestry practices like planting native
trees/shrubs on croplands can provide numerous benefits to the local
environment. Similarly to agrivoltaics, environmentally-friendly agroforestry (hereafter simply “agroforestry”) can contribute to climatechange mitigation, as the planted trees/shrubs can capture and store
atmospheric CO2 (Getnet et al., 2023; Terasaki Hart et al., 2023).
Another important benefit of agroforestry is its ability to reduce soil
erosion on farmlands with sloping terrain; Growing trees/shrubs along
contours can lead to the natural formation of terraces over time (Doet al., 2023; Hoang et al., 2017; Pattanayak and Evan Mercer, 1998;
Pellek, 1992). Agroforestry can also contribute to local biodiversity
conservation by, e.g., providing an alternative wood supply that reduces
harvesting from nearby forests (Tsegaye, 2023), and providing cover for
wildlife to safely move between habitats (Smith et al., 2013). Whenpracticed nearby inland water bodies, agroforestry can help protect thewater bodies and their shoreline areas by, e.g., reducing bank erosion,
surface runoff, and water pollution (by trapping sediments and pollutants), and providing shade to help regulate water temperatures (Graziano et al., 2022; Smith et al., 2013). Not all agroforestry practices areenvironmentally friendly, e.g., those that involve planting invasive alien
species or clear-cutting existing forests (Ollinaho and Kröger, 2021).
Environmentally-harmful agroforestry practices, however, are not thefocus of our study, and we do not advocate them as a desirable alternative to agrivoltaics.
Notably, agroforestry will need to be expanded to help meet new
global goals related to enhancing biodiversity and ecosystem services,
especially in areas which lack natural forests (Mulyoutami et al., 2023).
For example, in 2022, the parties to the Convention on Biological Diversity (CBD) agreed upon the “Kunming-Montreal Global Biodiversity
Framework”, which includes a series of goals for the 2030–2050 period.
Because of its ability to provide ecosystem services, agroforestry is
relevant for several of the Framework’s goals, e.g., Goal B: “Biodiversity
is sustainably used and managed and nature’s contributions to people,
including ecosystem functions and services, are valued, maintained andenhanced, with those currently in decline being restored… by 2050″ (CBD,2022). Efforts to enhance ecosystem services can be particularly effective when implemented in environmentally sensitive areas (ESAs), i.e.,areas important for the long-term maintenance of biodiversity, soil,

et al., 2023). Many shade-tolerant crops have also been found to bepromising for agrivoltaics, experiencing only minor reductions in yield
when under moderate shading from solar panels (Laub et al., 2022;
Widmer et al., 2024).
Not all types of crops or agricultural areas, however, are well-suited
for agrivoltaics. For example, shade-intolerant crops like maize are
typically unsuitable because they require abundant sunlight (Laub et al.,2022). Agricultural lands located far from electricity transmission
infrastructure or areas of high electricity demand (e.g., urban areas orother large settlements) are also often unprofitable for large-scale agrivoltaics due to high costs of connecting to the grid and transmitting thegenerated electricity long distances, respectively (Silva Herran andAshina, 2023). That said, smaller-scale (e.g., off-grid or micro-grid)
deployment of agrivoltaics in these areas have the potential to increase rural electrification rates, which remain low in some developing
countries (Gonocruz et al., 2023). Non-economic barriers also exist,
including community concerns that agrivoltaics disrupt the local landscape aesthetics (Maity et al., 2023), and farmers’ concerns over the(sometimes complex) regulatory processes required to add solar panels
to their croplands (Wagner et al., 2024).
1.2. Agroforestry and its potential competition with agrivoltaics
Analyses of the benefits and trade-offs of agrivoltaics have typically
been based on comparisons between agrivoltaic systems and open
cropping systems (in which there is no shading of crops). Often, however, areas suitable for agrivoltaics may also be suitable for othermultifunctional agriculture practices. Multifunctional agriculture activities are defined as those which serve additional purposes beyond
food and fibre provisioning, by, e.g., contributing to biodiversity conservation, natural resource management, and/or the socioeconomic
prosperity of rural areas (Renting et al., 2009). Agroforestry is a common type of multifunctional agriculture, typically characterized by the
integration of trees (and other woody permanent or perennial plants) incroplands. Importantly, because solar panels and trees (or other overstory woody vegetation) on cropland both cause shading of the vegetation below, agrivoltaics and agroforestry may compete with one
anotherforsuitable
croplands,
i.e.,
those
where
shade-loving/shade-tolerant crops grow well. Considering this, it isimportant that studies on agrivoltaics consider their benefits andtrade-offs relative to agroforestry, rather than open cropping systems
alone.
Agroforestry systems can be designed in various ways depending on
the local context. For example, hedgerow agroforestry systems involve
planting trees/shrubs along contours in sloping terrain (with crops
grown between the hedgerows) to minimize soil erosion (Fig. A1(a)).
Parkland agroforestry systems involve a more regular intermixing ofFig. 1. Tilted solar panels installed on platforms above crops. Photos from Chae et al. (2022) (a) and Yajima et al. (2023) (b).
2

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Resources, Conservation & Recycling 209 (2024) 107808

water, and other natural resources at the site and regional level (Jennings and Reganold, 1991). Thus, it may be sensible to promote thefuture expansion of agroforestry in ESAs. Examples of ESAs include
forests, wetlands, water bodies, other wildlife habitats, and the bufferzones surrounding these areas (which may contain croplands) (Ndubisi
et al., 1995).

2. Methods and materials
2.1. Study area
The 10 ASEAN member countries cover most of Southeast Asia, aregion known as a biodiversity hotspot as well as a hotspot for biodiversity loss (due to its high rate of deforestation and forest degradation)(Morand et al., 2017). It is also a region with rapidly increasing energy
demand. For example, ASEAN’s electricity consumption is expected totriple from 2020 to 2050 under a baseline scenario, to 3388 TWh/year(ASEAN Centre for Energy, 2022). Thus, there is a high demand for
expanding renewable energy supplies, including solar PV.
Southeast Asia is also the worlds’ primary home of agroforests,
containing 29 % of all global agricultural land with tree cover of at least
30 % (Van Noordwijk et al., 2020). One reason for this is that the region’s soil and climate are highly conducive to growing trees. As shownin the “tree carrying capacity” map in Fig. A2, nearly all of the land in
ASEAN is suitable for tree cover of at least 10 %, and most is suitable fortree cover of at least 30 % (Bastin et al., 2019). Thus, for the purpose ofthis study we assumed that agroforestry can potentially be implemented
on any herbaceous cropland (areas with extremely steep slopes or poor
soil may be unsuitable for agrivoltaics, but they are also unsuitable forgrowing crops).

1.3. Objective and related research
Our objective in this study was to analyse the potential of agrivoltaics, while also considering the simultaneous goal of expanding
agroforestry in ESAs. As the study area, we selected the 10 member
countries of the Association of Southeast Asian Nations (ASEAN): Brunei
Darussalam, Cambodia, Indonesia, Laos, Malaysia, Myanmar, thePhilippines, Singapore, Thailand, and Vietnam. ASEAN has a high energy demand, a high potential for solar PV (Siala and Stich, 2016), and alarge extent of natural ecosystems and croplands, so there is a need forresearch to support the environmentally-friendly deployment of agrivoltaics in the region.
A few prior studies have analysed the potential of solar PV in general,
or of agrivoltaics in particular, in ASEAN countries. Siala and Stitch(2016) estimated the total amount of land available for solar PV in
ASEAN considering all types of land use/land cover. Their study, however, only considered protected areas and wetlands as constraints
related to ESAs, and did not consider the need for buffer zones aroundthese ESAs to help protect them. Vidinopoulos et al., (2020) estimatedthe potential of agrivoltaics in ASEAN, assuming all agricultural land
was potentially available for agrivoltaics (i.e., without excluding land
located in ESAs). Gonocruz et al., (2022) conducted a more focusedstudy on the potential of agrivoltaics on rice farms in the Philippines,
and also assumed that all rice fields were potentially available foragrivoltaics. These past studies are valuable in that they show the
maximum potential of agrivoltaics in ASEAN countries. Our study, on
the other hand, intends to provide a more conservative estimate of thepotential of agrivoltaics, considering the simultaneous need forenhancing biodiversity/ecosystem services through agroforestry
expansion.
This research can be considered as a policy-screening scenario
analysis (IPBES, 2016) that considers two policy scenarios: One in which
any existing herbaceous cropland can potentially be used for agrivoltaics, and another in which croplands in ESAs are prohibited frombeing used for agrivoltaics. In the 2nd scenario, we assume that croplands in ESAs could instead be set aside as potential sites for agroforestry. The following types of ESAs were considered for this analysis:

2.2. Datasets used
We utilized several freely-available geospatial datasets with global
coverage for this research (Table A1), with the aim of developing amethodology that would be feasible and inexpensive to apply in othercountries/regions. Two main types of data were used: (1) a highresolution map of herbaceous croplands, and (2) high-resolution mapsof different types of ESAs.
For the cropland extent map, we used the 30 m resolution global map
produced by Potapov et al., (2021) for the year 2019. Cropland in thisdataset includes land used for growing annual/perennial herbaceous
crops for human consumption, forage (including hay), and biofuel. On
the other hand, land used for perennial woody crops, permanent pastures, and shifting cultivation are excluded from this dataset (Potapov
et al., 2021). Accordingly, we used this dataset to extract all areas
containing herbaceous cropland in ASEAN. Although other global mapsof agricultural areas exist, this particular dataset was selected for its highspatial resolution, high thematic accuracy (~85 % user’s and producer’s
accuracies in Southeast Asia (Potapov et al., 2021)), and because it excludes areas containing perennial woody crops (where agroforestry may
already be practiced). Permanent pastures are also potentially suitable
for agrivoltaics/agroforestry, but they are not considered in this study
because high-resolution and up-to-date maps of permanent pastures do
not yet exist at the global (or ASEAN) scale. The remaining datasets, alldescribed in Table A1, were used to identify ESAs in ASEAN countries,
and had similarly high spatial resolutions (~30 m or finer) to the global
cropland map.

i. Internationally recognized sites of high importance for biodiversity conservation, including protected areas (UNEP-WCMC
and IUCN, 2023), key biodiversity areas (BirdLife International,
2023; IUCN, 2016), and Ramsar wetland sites;
ii. Wetlands and permanent water bodies of 1 ha or larger in area;iii. Forests, defined as defined by FAO (FAO, 2010);
iv. Areas with slopes of greater than 15◦ , i.e., “steep” or “very steep”
slopes (FAO, 2006)
v. Buffer zones surrounding areas (i-iii).

2.3. Methods
2.3.1. Data preprocessing
First, all datasets in Table A1 were downloaded and projected to the“Asia South Albers Equal Area Conic” coordinate system to permit accurate area calculations. Further pre-processing was performed forseveral of the datasets. We extracted all wetlands of at least 1 ha in size
from the global wetland map (smaller areas were excluded because they
were typically not natural wetlands/water bodies). We generated a slope

The rationale for this analysis is that agrivoltaics, once installed, may
be cost-prohibitive to remove during the operational lifetime of the solar
panels (~25 years). This “lock-in” period of agrivoltaics should beconsidered in the renewable energy planning phase, to avoid deploying
agrivoltaics on land that may soon be needed for other purposes like
expanding agroforestry.

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map from the ALOS World 3D digital elevation model (Takaku et al.,2020), and extracted all pixels having a slope of >15◦ From the OpenStreetMap “waterways” dataset, we extracted all rivers and streams. The
global wetlands map also contained rivers and streams (mapped as“permanent water” (N. X. Zhang et al., 2023)), but we supplemented itwith the OpenStreetMap data because many narrow rivers/streams (e.
g., those with widths of less than ~30 m) were found to be missing. From
the PALSAR-2 Forest/Non-Forest map, we merged the two “forest”
classes (one with 10–90 % crown cover, and one with >90 % crowncover (JAXA, 2022)) into one because the result matches the FAO’s
definition of “forest” (≥10 % crown cover) (FAO, 2010). Several otherglobal forest maps exist, but we used the PALSAR-2 map because it is themost consistent with FAO’s definition of “forest” (Johnson et al., 2023).
Finally, from all of the global datasets, we extracted the data for each
ASEAN country using the national boundaries from the global administrative areas dataset.

agrivoltaics after excluding the ESAs.
2.3.3. Calculating the technical potential of agrivoltaics
Next, we calculated technical potential of agrivoltaics, defined as theamount of power that can be generated considering a particular type ofagrivoltaic technology (e.g., the solar panel specifications and solar
tracking capabilities) and layout (e.g., panel spacing) within thegeographical potential (McKenna et al., 2022). The technical potentialof agrivoltaics by country, in TWh/year, was calculated by:Technical potential = geographic potential x electricity yield per ha(1)Electricity yield per ha = installed capacity per ha x capacity factor
x 8760(2)
Where 8760 is the number of hours in a year. For estimating the
installed capacity, we assumed the same agrivoltaics system and layout
as Schindele et al., (2020), i.e., SolarWorld SW270 duo bifacial PV
modules aligned in rows, with the module row spacing being 27 %greater than that of a conventional solar PV power plant (to allow forsufficient photosynthetically active radiation for crops below the solar
panels), and no solar tracking capability. Based on these specifications,the installed capacity is 519.18 kWp/ha (Schindele et al., 2020). For thecapacity factor, i.e., the actual power output compared with the
maximum theoretical output of 8760 h of full sunshine/year, we used
the average values for “cropland/natural vegetation” areas calculated by
Siala and Stich (2016) (Table 1). Eq. (1) gives the total annual electricity
generation for each country, assuming all croplands within thegeographic potential are used for agrivoltaics. As already mentioned,
however, agrivoltaics is only profitable for some croplands. Thus, as wasdone in other previous studies (Adeh et al., 2019; Jamil et al., 2023;
Yeligeti et al., 2023), we calculated the technical potential assuming
only a small percentage (1–10 %) of the geographic potential will
actually be used for agrivoltaics.

2.3.2. Calculating the geographic potential of agrivoltaics
Next, we calculated the geographical potential of agrivoltaics, i.e., thetotal amount of herbaceous cropland available after accounting for
geographic constraints (McKenna et al., 2022). The geographic constraints considered were the ESAs identified in Section 2.3.1. and theirbuffer zones. The workflow for calculating the geographic potential isshown in Fig. 2.
Buffer zones surrounding each ESA were generated using QGIS
software, version 3.28.2. Areas considered to be internationallyrecognized sites of importance for biodiversity conservation, including
protected areas, key biodiversity areas, and Ramsar wetland sites(Table A1), were assigned a buffer zone of 1 km, assuming that croplands within these buffer zones could alternatively be used for agroforestry to allow for enhanced the biodiversity and ecosystem services ofthese sites. Keeping a large buffer around these important sites is also
helpful to allow for the potential expansion of the biodiversity sites in
the future (Sweeney and Newbold, 2014). This is an important consideration given that one of the Kunming-Montreal Global Biodiversity
Framework’s goals is to expand the extent of protected areas (and othereffective area-based conservation measures (OECMs)) to at least 30 % of
all terrestrial, inland water, coastal and marine areas by 2030 (CBD,2022). In comparison, the current global extent of protected areas andOECMs is only 17.22 % (https://www.protectedplanet.net/en, lastaccessed December 15, 2023), with croplands occupying approximately
6 % of protected areas (Vijay and Armsworth, 2021).
All other ESAs in Table A1, including wetlands, rivers/streams, andforests, were assigned a smaller buffer zone of 100 m. This buffer distance was selected considering that, for wetlands/rivers/streams, bufferzones of 100 m or smaller can effectively remove many pollutants insurface runoff as well as provide organic materials from trees to theseareas and regulate water temperatures (Graziano et al., 2022; Sweeney
and Newbold, 2014). After generating the maps of the ESAs, including
their respective buffer zones, we overlaid them on the 30 m resolution
cropland map. All overlapping cropland areas were identified, allowing
us to calculate the how much cropland was potentially available for3. Results and discussion
3.1. Geographic potential of agrivoltaics on herbaceous croplands
After excluding ESAs, the geographic potential of agrivoltaics onherbaceous croplands in ASEAN was 369,841 km2, which represents 68% of the initial cropland extent (541,998 km2). The remaining 32 %
(172,157 km2) of herbaceous croplands, on the other hand, weredeemed as potential sites for agroforestry expansion due to their location
within ESAs. There was significant variation in the degree to which
excluding ESAs reduced the geographic potential of agrivoltaics in eachcountry (Table 1). For example, the geographic potential in Malaysia,
Indonesia, and Vietnam was reduced by only 20–30 %, while at the otherextreme, in Brunei and Singapore it was reduced by 89 % and 63 %,respectively. This was due to variations in the extent of ESAs, as well as
their spatial configuration relative to croplands, in each country.
In terms of the total amount of land available for agrivoltaics,

Fig. 2. Workflow for calculating geographic potential of agrivoltaics under a scenario where they are prohibited in environmentally sensitive areas.
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Table 1
Average capacity factor of “cropland/natural vegetation” land in each ASEAN country, from Siala and Stich (2016), and geographic potential of agrivoltaics in eachcountry after sequentially excluding each type of environmentally sensitive area.
Country

Average
capacityfactor(%)

Initial extent of
herbacious cropland(km2)

Cropland farther than 1 km frominternationally-recognised
biodiversity sites(km2)…And, farther than 100 mfrom wetlands, rivers, or
streams(km2)…And, farther than
100 m from forests(km2)…And 15◦ orlower slope

Brunei
Cambodia
Indonesia
Laos
Malaysia
MyanmarPhilippines
Singapore
Thailand
Vietnam
Total

14.6
14.5
14.9
14.6
14.3
14.3
14.3
13.8
14.5
14.1

3
59,69987,674
13,602
3075
117,392
36,6795
173,777
50,093541,9981
47,56882,45911,2292958
106,986
32,016
4
158,224
44,605
486,049

1
43,121
76,295
10,5982772
100,264
27,941
3
153,142
42,054456,1901
38,15771,9297582
2253
88,314
18,031
2
111,151
35,748373,1630.36
38,157
69,3267582
2253
88,101
17,8792
111,077
35,463369,841

Fig. 3. Map of herbaceous croplands in ASEAN after excluding those located in environmentally sensitive areas (ESAs). This represents the geographic potential ofagrivoltaics in ASEAN. The initial cropland extent is shown for comparison (extracted from Potapov et al. (2021)).

Thailand (111,077 km2), Myanmar (88,101 km2), and Indonesia(69,326 km2) had the most, together accounting for 73 % of the entire
geographic potential of ASEAN. In contrast, Brunei and Singapore hadvery little land available for agrivoltaics (0.36 km2 and 2 km2, respectively), due to both the relatively large extent of ESAs and small initialcropland extent in these countries. The final map of the geographic
potential of agrivoltaics in ASEAN countries is shown in Fig. 3, and thedata is available for download in shapefile (.shp) format at https://www.iges.or.jp/en/pub/maps-agrivoltaics/en.
Interestingly, as each additional type of ESA was added as a new
constraint, the change in the amount of available land for agrivoltaics
became smaller. For example, the final ESA included as a geographic
constraint in our study, areas with slopes >15◦ , had little-to-no impact
on the amount of land available for agrivoltaics in Cambodia/Laos/
Malaysia/Myanmar/Vietnam, despite the fact that many croplands inthese countries were located in areas having slopes >15◦ This was
because many croplands were located within multiple overlapping ESAs,e.g., within the buffer zone of a forest area and on land with a slope of>15◦ These results suggest that, because many different types of ESAs

overlap, including even more types of ESAs as constraints for agrivoltaics may have a relatively limited impact on the estimatedgeographic potential. Considering these results, it may also be desirable
to prioritize agroforestry in areas where multiple ESAs overlap (tomaximise the benefits agroforestry provides to these important environments), and this could be an interesting topic for future agroforestry
studies.
Land-use change is prevalent in many ASEAN countries, especiallyurban expansion (Johnson et al., 2021; Potapov et al., 2022), and thesechanges could affect the geographic potential of agrivoltaics in different
ways. For example, expansion of protected areas, forests, and/or wetlands in the future would lead to a further increase in the extent of ESAs
in ASEAN, which would reduce the amount of cropland available foragrivoltaics (assuming our methodology is followed). Conversion ofcroplands to built-up/urban or other land uses would also reduce theland available for agrivoltaics. On the other hand, loss of forest areas or
wetlands could potentially lead to an increase in the amount of available
cropland for agrivoltaics by, e.g., reducing the extent of ESAs and/or
increasing the total cropland extent (if forest areas or wetlands are5

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Table 2
Technical potential of agrivoltaics on herbaceous croplands in each ASEAN country, after sequentially excluding each type of environmentally sensitive area. Thisestimate assumes 1 % [or 10 %] of the geographic potential of cropland (See Table 1) will actually be utilised for agrivoltaics.
Country

Annual electricity generation using 1 %[or 10 %] of initial extent of herbacious
cropland (TWh)Limited to cropland farther than 1
km from "designated" biodiversity
sites (TWh)…And farther than 100 m fromwetlands, rivers, and streams(TWh)…And farther than 100
m from forests (TWh)…And 15◦ orlower slope(TWh)

Brunei0.00[0.00]
39.37[393.70]59.41[594.13]9.03[90.32]2.00[20.00]76.35[763.48]23.85[238.55]
0.00[0.03]
114.60[1145.9]
32.12[321.23]356.74[3567.44]

0.00[0.00]
31.37[313.69]55.88[558.79]
7.46[74.56]
1.92[19.24]69.58[695.80]
20.82[208.22]0.00[0.02]
104.34[1043.43]
28.60[286.04]
319.98[3199.80]

0.00[0.00]
28.44[284.37]51.70[517.02]
7.04[70.37]1.80[18.03]65.21[652.08]18.17[181.72]
0.00[0.02]
100.99[1009.91]
26.97[269.68]
300.32[3003.20]

0.00[0.00]
25.16[251.63]48.74[487.43]
5.03[50.35]
1.47[14.66]57.44[574.36]
11.73[117.27]0.00[0.01]
73.30[733.00]
22.92[229.24]245.79[2457.94]

0.00[0.00]
25.16[251.63]46.98[469.79]
5.03[50.35]
1.47[14.66]57.30[572.98]
11.63[116.28]0.00[0.01]
73.25[732.51]22.74[227.42]243.56[2435.62]

Cambodia
Indonesia
Laos
Malaysia
MyanmarPhilippines
Singapore
Thailand
VietnamTotal

converted to croplands). Future research may focus on how projectedland-use/land-cover changes could affect the geographic potential ofagrivoltaics.

actually used for agrivoltaics, the technical potential of agrivoltaics onherbaceous croplands in ASEAN is estimated to be 243.56–2435.62
TWh/year (Table 2). In comparison, official projections of ASEAN’s
electricity generation for the year 2050, as described in ASEAN Centre
for Energy (2022), range from 2108 to 3388 TWh/year. The “ASEAN
Plan of Action for Energy Cooperation (APAEC) Regional Targets Scenario”, which projects 2108 TWh/year electricity generation in 2050, is
the most stringent scenario that has been officially announced by
ASEAN, and assumes that various regional policies will be put in place toenhance energy efficiency. Our results indicate that agrivoltaics cantheoretically supply 12–116 % of the total electricity generated under
this scenario (Table 3). This, of course, assumes an ideal situation wherecountries’ power grids are fully interconnected, and power can be stored
and flexibly used when it is needed.
Cambodia and Myanmar can theoretically achieve all of their electricity generation needs (also based on the APAEC Regional Targets
Scenario) if less than 1 % of its geographic potential is used for agrivoltaics, while Lao PDR and Thailand can also be self-sufficient if lessthan 10 % of their geographic potential is used (and Vietnam can satisfy
99 % of its projected electricity generation). Notably, Cambodia,
Myanmar, and Thailand can all generate more than 10 times theirprojected electricity generation if 10 % of their geographic potential isused, indicating that they could also potentially serve as energy exporters to help neighbouring countries like Brunei, Singapore, andMalaysia meet their demand. Along these lines, cross-border transmission systems and energy trading schemes are being developed/
enhanced through the ASEAN Power Grid (Aris and Jørgensen, 2020;
International Renewable Energy Agency and ASEAN Centre for Energy,2022).
Due to the variable nature of solar PV power, ASEAN countries alsoutilize power generated from other renewable sources like wind and hydropower, and several projections of ASEAN’s future renewable energy
mix exist. For further context, we also compare our results with different
projections of ASEAN’s total solar PV power generation in 2050. Table 4shows the 2050 renewable energy share and solar PV power generation
estimated under 11 different scenarios developed by the ASEAN Centre forEnergy, the International Renewable Energy Agency, and Handayani et al.
(2022). According to these scenarios, ASEAN’s 2050 projected solar PV
power generation varies widely, from less than 130 TWh/year to nearly
3000 TWh/year. Our estimates indicate that using 10 % of the geographic

3.2. Technical potential of agrivoltaics on herbaceous croplands
Based on an assumption that 1–10 % of the geographic potential is
Table 3Comparison of electricity generation potential of agrivoltaics with projections ofelectricity consumption under the APAEC target scenario (ASEAN Centre forEnergy, 2022). The ASEAN centre for energy kindly provided us with projections
for each country, as only regional-level projections were listed in their originalreport (ASEAN Centre for Energy, 2022).
Country

2020
(TWh)

2050,
Baseline
scenario(TWh)2050, APAECRegional Targets
Scenario (APS)(TWh)

Agrivoltaics
potential
relative to
APAEC Regional
Target Scenario,
if 1 % [or 10 %]of thegeographic
potential is used

Brunei

4.73

7.78

8.99

Cambodia

11.09

30.95

17.34

Indonesia

275.58960.79642.27

Laos

7.78

44.95

48.21

Malaysia

152.13485.97220.88Myanmar

20.29

42.04

32.91

Philippines

83.24

359.46231.65Singapore

50.78

90.31

67.76

Thailand

187.26461.44355.11

Vietnam

218.02904.27483.16

Total

1010.913388.00

2108.00

0%
[0 %]
145 %
[1451 %]
7%
[73 %]
10 %[104 %]
1%
[7 %]
174 %
[1741 %]
5%
[50 %]
0%
[0 %]
21 %
[206 %]
5%[47 %]
12 %[116 %]

6

B.A. Johnson et al.

Resources, Conservation & Recycling 209 (2024) 107808

Table 4
Different projections of solar PV power generation in 2050, compared with the technical potential of agrivoltaics estimated in this study.
SourceScenario

Total power generationin 2050 (TWh)Renewables share in
2050 (%)

Solar PV power generationin 2050 (TWh)Agrivoltaics potentialb/total solar PV
power generation (%)

ASEAN Centre forEnergy (2022)

Baseline
ASEAN member statesnational targets
APAEC regional targets
Least-cost optimisation
Planned energy
Transforming energy
1.5-S RE90a
1.5-S RE100a
Reference
Renewable energy
Net zero emissions

3388
2566

35.0
49.3

131.6
129.9

1868.7 %1893.2 %

2108
2114
3797
4697
5128
5128
3715
3715
3715

63.2
57.6
77.0
86.0
88.0
99.0
38.0
50.0
93.0

159.3
NA
1374.8c
2121.7c2585.3c2945.8c185.8
442.1
2266.2

1543.8 %NA
178.9 %
115.9 %
95.1 %
83.5 %
1323.6 %556.3 %
108.5 %IRENA and ACE(2022)
Handayani et al.
(2022)a1.5 ◦ C scenarios aiming to reach net-zero emissions globally by 2050, one with 90 % renewable power generation (1.5-S RE90) and one with 100 % renewable
power generation (1.5-S RE100) (International Renewable Energy Agency and ASEAN Centre for Energy, 2022).
b
Assuming 10 % of the geographic potential is used for agrivoltaics.
c
Calculated based on the installed capacity of solar PV, assuming an average capacity factor of 14 %.

Relatively large uncertainty exists with regards to the agrivoltaics
technologies that will be used by ASEAN countries in the future. Solar PV
conversion efficiency is constantly increasing, so the efficiency assumed
for our estimates of the technical potential of agrivoltaics (which are basedon the current technology) are likely much lower than what will beachievable in the near future. Our analysis also assumed fixed PV panels,
while the use of panels with solar tracking systems may lead to higher
energy generation (although they have higher costs as well) (Ali Khan
Niazi and Victoria, 2023). In addition, our assumptions regarding the
spacing of solar PV panels in agrivoltaics systems are based on current
technologies that cause shading of the understory crops, while (semi-)transparent PV panels are in development that could allow for denser
installation of PV panels on croplands as well as the use of agrivoltaics inareas with less shade-tolerant crops (although the additional costs may be
an issue for this as well) (Stallknecht et al., 2023). On the other hand,because our estimates of the installed capacity (kWp/ha) of agrivoltaics
are based only on the specifications and spacing of the solar panels on the
agricultural lands (Schindele et al., 2020), a final source of uncertainty is
the amount of additional land that would be required for other components of agrivoltaics systems, e.g., inverters, energy storage systems,
and/or monitoring systems (Maity et al., 2023).

potential of agrivoltaics on herbaceous croplands can theoretically allow
for ASEAN to meet/exceed the projected solar PV power generation in all
but two scenarios, “1.5-S RE90” and “1.5S RE100” (Handayani et al.,2022), which assume 90 % and 100 % renewable energy generation,
respectively. This gives further evidence that the technical potential for
agrivoltaics on herbaceous croplands is large, and importantly, that it does
not to conflict with the goal of conserving/enhancing biodiversity andecosystem services in the region. Future research could consider if/how
ASEAN can meet even these most ambitious scenarios by supplementing
agrivoltaics with other solar PV technologies that can also be integrated
with the existing land uses, e.g., rooftop solar or floating solar PV (Almeida
et al., 2022; Yeligeti et al., 2023). For this to be realized, however, batteries and other means of storing the generated solar power would alsoneed to be significantly scaled up.
3.3. Sources of uncertainty
The main sources of uncertainty in our study relate to errors in thegeospatial datasets used to estimate the geographic potential of agrivoltaics, and uncertainties in the agrivoltaics technologies used. An accuracy assessment of the herbaceous croplands map used in this study
indicated that it had an overall accuracy of 96.6 % for the “South-east
Asia” region, and underestimated the actual extent of herbaceous croplandby 3.6 % (Potapov et al., 2021). If we assume a similar accuracy for only
the croplands located outside of ESAs, our estimates of the geographic andtechnical potential of agrivoltaics may also be slightly underestimated.
The maps of wetlands and forest areas we used to identify ESAs have reported overall accuracies of 86.4 % (Zhang et al., 2023) and 90.4 % (JAXA,
2022), respectively, while the elevation data used to identify areas withsteep slopes had a root mean square error of 3.27 m in height (Takaku
et al., 2020). The remaining maps used to identify ESAs were generated bymanually digitising polygons, so their accuracy should be quite high(although quantitative accuracy assessment results are unavailable).
Because the final map of ESAs in this study was a union of these individual
maps, however, not all errors were propagated to our final estimates of thegeographic/technical potential of agrivoltaics. Errors in the individual
maps could be eliminated in areas where multiple maps of ESAs wereoverlapping, as long as one of the maps was classified accurately, and as
discussed in Section 3.1., there was a high degree of overlap among thedifferent maps of ESAs. Finally, the capacity factor values we used to
calculate electricity yield per ha were average values for each country(Siala and Stich, 2016), so they may be somewhat over- or underestimatedat the subnational level, depending on the local meteorological conditions.
If other researchers intend to use our final map or herbaceous croplands
outside ESAs to calculate the technical potential of agrivoltaics at the
subnational level, it is recommended to use finer resolution meteorological
data to calculate the appropriate capacity factor(s).

4. Conclusions
Agrivoltaics has the potential to meet a significant share of ASEAN’s
projected electricity generation needs, even if its deployment is prohibited on croplands located in ESAs. Utilizing just 10 % of thegeographic potential of agrivoltaics would allow it to theoretically fulfill
ASEAN’s projected solar PV generation needs under all but the mostambitious renewable energy scenarios (i.e., 1.5-S RE90 and 1.5-S RE100
in Table 4). Notably, the future expansion of agrivoltaics, even at such alarge scale, does not need to conflict with the simultaneous goal ofenhancing biodiversity and ecosystems services through expanding
agroforestry. Some ASEAN countries can generate more power withagrivoltaics than they need, while others can generate very little, so
further enhancement of transmission infrastructure like the ASEAN
Power Grid would be essential to ensure that the power generated fromagrivoltaics can reach where it is needed. Our map of herbaceous
croplands located outside ESAs (https://www.iges.or.jp/en/pub/m
aps-agrivoltaics/en) can help with the identification of potential sites
for agrivoltaics, but local environmental impact assessments and community consultations would still be important to ensure that negativeenvironmental and social impacts are minimized. Finally, our study
demonstrates that it can be useful to analyse the benefits/trade-offs ofagrivoltaics as compared with multifunctional agricultural systems like
agroforestry, rather than just open croplands.

7B.A. Johnson et al.

Resources, Conservation & Recycling 209 (2024) 107808

CRediT authorship contribution statement

interests or personal relationships that could have appeared to influence
the work reported in this paper.

Brian A. Johnson: Validation, Visualization, Writing – originaldraft, Investigation, Methodology, Data curation, Formal analysis,
Conceptualization. Yosuke Arino: Writing – review & editing, Writing –original draft, Resources, Project administration, Investigation, Funding
acquisition, Formal analysis, Conceptualization. Damasa B. MagcaleMacandog: Writing – review & editing, Visualization, Resources,
Formal analysis, Data curation. Xianbing Liu: Writing – original draft,
Visualization, Validation, Formal analysis, Data curation, Conceptualization. Makino Yamanoshita: Writing – review & editing, Writing –original draft, Investigation, Conceptualization.

Data availability
Data will be made available on request.
Acknowledgements
This work was supported by the Environmental Research andTechnology Development Fund [JPMEERF20221C06] of the Environmental Restoration and Conservation Agency of Japan.

Declaration of competing interest
The authors declare that they have no known competing financial

AppendicesFig. A1. Examples of hedgerow agroforestry (photo by authors) (a), parkland agroforestry (photo by authors) (b), and riparian agroforestry (photo from Prastiyo
et al. (2020)) (c).

8

B.A. Johnson et al.

Resources, Conservation & Recycling 209 (2024) 107808

Fig. A2. Map showing the amount of tree cover that could potentially exist with minimal human intervention, created by the authors using the “earth’s tree carrying
capacity” map (Bastin et al., 2019).
Table A1
Datasets used in this study.
DatasetDescription

Usage in this study

Time period

Spatial resolution(for remotesensing-derived
maps)

Link to dataset

1. University of Maryland
30 m global croplandmap (Potapov et al.,2021)
2. World database on
protected areas (UNEPWCMC and IUCN, 2023)

Map of land used for annual and perennial
herbaceous crops.

Extracting herbaceous
croplands

2019

30m

https://glad.umd.edu/dataset/
croplands (last accessed November
13, 2023)

Areas where special measures need to betaken to conserve biological diversity(Secretariat of the Convention on Biological
Diversity, 1992, p. 8)
Sites of importance for the global
persistence of biodiversity (IUCN, 2016)

Identifying ESAs:
protected areas andtheir 1 km buffer zone

Until April2023

http://protectedplanet.net/ (lastaccessed April 13, 2023)

Identifying ESAs: key
biodiversity areas andtheir 1 km buffer zone
Identifying ESAs:
Ramsar wetland sites
and their 1 km buffer
zone
Identifying ESAs:
wetlands of at least 1 hain size, and their 100 m
buffer zone
Identifying ESAs:
rivers/streams andtheir 100 m buffer zone
Identifying ESAs:
forests and their 100 m
buffer zone

Until April
2023
Until
September
2023

n/a(manually
digitised
polygons)
n/a (manually
digitised
polygons)
n/a (manually
digitised
polygons)

2020

30m

https://zenodo.org/records/7340516 (last accessed November
10, 2023)

Until May
2023
2020

n/a (manually
digitized
polygons)
25m

https://download.geofabrik.de/asia.html (last accessed May 09,2023)
https://developers.google.com/ea
rth-engine/datasets/catalog/JA
XA_ALOS_PALSAR_YEARLY_FNF4.
html (last accessed September 26,
2023)

Identifying ESAs: areas
with a slope of morethan 15◦

2011

30m

https://developers.google.com/ea
rth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V3_2 (lastaccessed February 06, 2024)

3. Key biodiversity areas(BirdLife International,2023)
4. Ramsar wetland sites

Wetland site of international importance

5. Global 30 m wetland map
with fine classification
system (X. N. Zhang et al.,2023)
6. OpenStreetMap “rivers”
and “streams”

Map of other wetlands, including:permanent water bodies, swamps, marshes,
flooded flats, saline wetlands, mangroves,
salt marshes, and tidal flats.
Rivers and streams extracted fromOpenStreetMap

7. PALSAR-2 Forest/nonforest map, Version 2(JAXA, 2022)“Land spanning more than 0.5 ha with trees
higher than 5 m and a canopy cover of morethan 10 percent, or trees able to reach thesethresholds in situ. It does not include landthat is predominantly under agricultural or
urban land use“ (FAO, 2010)
Digital elevation model.

8. ALOS World 3D – 30 m
(AW3D30), Version 3.2(Takaku et al., 2020)9https://www.keybiodiversityareas.org/kba-data/request (lastaccessed April 13, 2023)
https://rsis.ramsar.org/ (lastaccessed September 19, 2023)

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Resources, Conservation & Recycling 209 (2024) 107808

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