Agricultural intensity for sustainable regional development: A case study in peri-urban areas of Karawang Regency, Indonesia

doi:10.1016/j.regsus.2024.100117

Abstract

Abstract:

Peri-urban areas are playing an increasingly crucial role in the agricultural development and intensification in Indonesia. Peri-urban agriculture is highly vulnerable to change compared with urban and rural agriculture, due to its location in transitional areas. Indicators of peri-urban agricultural intensity can help guide regional development. In this study, we assessed the sustainability of peri-urban areas based on agricultural intensity in Karawang Regency, Indonesia. We developed a village-based index to assess the region’s agricultural intensity by rescaling the village agriculture index (VAI) and combining the local sustainability index (LSI) with factor analysis. Since the unit of analysis is the village, we modified the LSI to the village sustainability index (VSI). In addition, we also developed a logical matrix analysis to determine the level of agricultural sustainability (LoAS) of each village. The combined results of the three indices (VAI, VSI, and LoAS) generated information about agricultural sustainability. The results indicated that peri-urban villages with high agricultural intensity tended to exhibit low levels of social welfare, economic development, and disaster risk. Moreover, high agricultural intensity did not necessarily ensure the prosperity of the people. Instead, there was the economic disparity among the villages in the study area. Encouraging diversity of agricultural intensity seems to be more critical than promoting agricultural intensity itself. Overall, this study highlights the distinctive characteristics and dynamic of peri-urban areas. New approaches, variables, and information regarding the combination of agricultural intensity and sustainability need to be developed as valuable tools for regional planning.

Key words: Agricultural intensity, Village agriculture index (VAI), Village sustainability index (VSI), Level of agricultural sustainability (LoAS), Peri-urban areas;Indonesia

Setyardi Pratika MULYA, Delik HUDALAH. Agricultural intensity for sustainable regional development: A case study in peri-urban areas of Karawang Regency, Indonesia[J]. Regional Sustainability, 2024, 5(1): 100117.

Figures/Tables 14

Fig. 1.

Table 1

Variables used in scalogram analysis."

Concept	Variable^*	Formula and description	Source
	Total population (persons)	Original data	MoHA (2019)
Utilization	Conversion of agricultural land to non-agricultural land during 2017-2021 (hm²)	$ L C_{2021}-L C_{2017}$, where LC₂₀₂₁ and LC₂₀₁₇ represent land cover (hm²) in 2021 and 2017, respectively.	Zanaga et al. (2021)
	Percentage of land suitable for agriculture (%)	$ \frac{\text { Land area of land suitable for agriculture }}{\text { VA }} \times 100 \%$, where VA is the village area (hm²).	Wall (1989)
	Average ownership of rice fields in 2014 (m²)	$ \sum \text { Opf / nfarmer }$, where ∑Opf represents the area of rice fields owned by each farmer (m²); and nfarmer is the total number of farmers who grow rice in the village.	Central Statistics Agency of Indonesia (2014)
	Average ownership of non-rice fields in 2014 (m²)	$ \sum \text { Nlof / nfamer } N R F$, where ∑Nlof represents the area of non-rice fields owned by each farmer (m²); and nfarmerNRF is the total number of farmers who do not grow rice in the village.	Central Statistics Agency of Indonesia (2014)
	Average planted area per village (m²)	$ \sum R f p f / \text { nfanmer }$, where ∑Rfpf represents the area of rice fields planted by each farmer (m²); and nfarmer is the total number of farmers who grow rice in the village.	Central Statistics Agency of Indonesia (2021b)
	Percentage of non-rice fields per village (%)	$ \frac{n r f a}{\mathrm{VA}} \times 100 \%$, where nrfa represents the area of non-rice field (hm²); and VA is the village area (hm²).	MoEF (2020); Zanaga et al. (2021)
	Percentage of rice fields per village (%)	$ \frac{r f a}{\mathrm{VA}} \times 100 \%$, where rfa represents the area of rice field (hm²); and VA is the village area (hm²).	MoA (2019)
	Percentage of spatial planning area planned for agriculture per village (%)	$ \frac{\text { lapff }}{\mathrm{VA}} \times 100 \%$, where lapff represents the area of land planned for farming (hm²); and VA is the village area (hm²).	Public Works and Housing Office (2013)
	Percentage of agricultural land aligned with spatial planning (%)	$ \frac{\text { fasp }}{\mathrm{VA}} \times 100 \%$, where fasp represents the area of land aligned with spatial planning (hm²); and VA is the village area (hm²).	Public Works and Housing Office (2013); Zanaga et al. (2021)
	Percentage of forest area aligned with spatial planning (%)	$ \frac{\text { faasp }}{\mathrm{VA}} \times 100 \%$, where faasp represents the area of forest aligned with spatial planning (hm²); and VA is the village area (hm²).	Public Works and Housing Office (2013); MoEF (2019)
Production	Existence of irrigation channels as a source of agricultural water	(1=yes, 0=no)	Central Statistics Agency of Indonesia (2021b)
	Existence of lakes or sites as agricultural water sources	(1=yes, 0=no)	Central Statistics Agency of Indonesia (2021b)
	Presence of a shop selling village unit cooperative-owned crops	(1=yes, 0=no)	Central Statistics Agency of Indonesia (2021b)
	Number of farmer groups per village	Original data	Central Statistics Agency of Indonesia (2021b)
	Number of irrigation institutions per village	Original data	Central Statistics Agency of Indonesia (2021b)
	Percentage of households in each village engaged in agriculture (%)	$ \frac{n f h}{n h} \times 100 \%$, where nfh represents the number of farm households; and nh is the number of households.	MoHA (2019)
Production	Empowerment of productive business management in agricultural and non-agricultural industries	(1=yes, 0=no)	Central Statistics Agency of Indonesia (2021b)
Production	Construction of agricultural facilities	(1=yes, 0=no)	Central Statistics Agency of Indonesia (2021b)
Impact	Main source of income for most farmers	(1=agriculture, other=non-agriculture)	Central Statistics Agency of Indonesia (2021b)
Impact	Existence of village flagship or main products	(1=yes, 0=no)	Central Statistics Agency of Indonesia (2021b)

Table 1

Table 2

Fig. 2.

Table 3

Table 4

Fig. 3.

Table 5

Table 6

Table 7

Fig 4.

Fig. 5.

Fig. 6.

Fig. 7.

References 80

[1]	Akaeze, O., Nandwani, D., 2020. Urban agriculture in Asia to meet the food production challenges of urbanization: A review. Urban Agric Reg Food Syst. 5, doi: 10.1002/uar2.20002.
[2]	Bai, X., Wieczorek, A.J., Kaneko, S., et al., 2009. Enabling sustainability transitions in Asia: The importance of vertical and horizontal linkages. Technol. Forecast. Soc. Chang. 76(2), 255-266. doi: 10.1016/j.techfore.2008.03.022
[3]	Beckers, V., Poelmans, L., Van Rompaey, A., et al., 2020. The impact of urbanization on agricultural dynamics: A case study in Belgium. J. Land Use Sci. 15(5), 626-643. doi: 10.1080/1747423X.2020.1769211
[4]	Bongiovanni, R., Lowenberg-Deboer, J., 2004. Precision agriculture and sustainability. Precis. Agric. 5, 359-387.
[5]	Bugu, Z.Y., Yucheng, H., 2018. An empirical analysis of the factors affecting the profitability of China’s agricultural listed companies under the background of agricultural modernization. International Journal of Applied Economics, Finance and Accounting. 2(1), 19-26. doi: 10.33094/8.2017
[6]	Central Statistics Agency of Indonesia, 2014. Socio-Economic Analysis of Farmers in Indonesia. Jakarta: Central Statistics Agency of Jakarta, 12-24 (in Indonesian).
[7]	Central Statistics Agency of Indonesia, 2019. Karawang Regency in Figures 2019. Karawang: Central Statistics Agency of Karawang Regency, 193-195 (in Indonesian).
[8]	Central Statistics Agency of Indonesia, 2021a. West Java Province in Figure 2021. Bandung: Central Statistics Agency of West Java Province (in Indonesian).
[9]	Central Statistics Agency of Indonesia, 2021b. Village Potential Statistics of Indonesia. Jakarta: Central Statistics Agency of Indonesia (in Indonesian).
[10]	Central Statistics Agency of Indonesia, 2023. Karawang Regency in Figures 2023. Karawang: Central Statistics Agency of Karawang Regency, 57-58 (in Indonesian).
[11]	de Bruin, S., Holleman, C., 2023. Urbanization is Transforming Agrifood Systems across the Rural-Urban Continuum Creating Challenges and Opportunities to Access Affordable Healthy Diets. [2023-12-25]. https://www.fao.org/3/cc8094en/cc8094en.pdf.
[12]	Delavarpour, N., Koparan, C., Nowatzki, J., et al., 2021. A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sens. 13(6), 1204, doi: 10.3390/rs13061204.
[13]	Diehl, J.A., Sweeney, E., Wong, B., et al., 2020. Feeding cities: Singapore’s approach to land use planning for urban agriculture. Global Food Security. 26, 100377, doi: 10.1016/j.gfs.2020.100377.
[14]	Diogo, V., Helfenstein, J., Mohr, F., et al., 2022. Developing context-specific frameworks for integrated sustainability assessment of agricultural intensity change: An application for Europe. Environ. Sci. Policy. 137, 128-142.
[15]	FAO, 2016. Urbanization, Rural Transformation and Implications for Food Security and Nutrition. [2023-12-20]. https://www.fao.org/fileadmin/templates/cfs/Docs1516/Urb_RuralTransf/CFS_UrbRurTechWkshpAgenda_08_Feb.pdf.
[16]	Fernando, M., Samita, S., Abeynayake, R., 2012. Modified factor analysis to construct composite indices: Illustration on urbanization index. Tropical Agricultural Research. 23(4), 327-337. doi: 10.4038/tar.v23i4.4868
[17]	Harris, J.M., 1996. World agricultural futures: Regional sustainability and ecological limits. Ecol. Econ. 17(2), 95-115. doi: 10.1016/0921-8009(96)00020-1
[18]	Hu, S., Yang, Y., Zheng, H., et al., 2022. A framework for assessing sustainable agriculture and rural development: A case study of the Beijing-Tianjin-Hebei region, China. Environ. Impact Assess. Rev. 97, 106861, doi: 10.1016/j.eiar.2022.106861.
[19]	Huang, C.L., Qu, S., Gao, B., et al., 2019a. Effects of urbanization on phosphorus metabolism in a typical agricultural area. J. Clean Prod. 214, 803-815.
[20]	Huang, Z., Du, X., Castillo, C.S.Z., 2019b. How does urbanization affect farmland protection? Evidence from China. Resources, Conservation and Recycling. 145, 139-147.
[21]	Hudalah, D., Winarso, H., Woltjer, J., 2007. Peri-urbanisation in East Asia: A new challenge for planning? Int. Dev. Plan. Rev. 29(4), 501-517.
[22]	Hudalah, D., Firman, T., 2012. Beyond property: Industrial estates and post-suburban transformation in Jakarta Metropolitan region. Cities. 29(1), 40-48. doi: 10.1016/j.cities.2011.07.003
[23]	Hudalah, D., Viantari, D., Firman, T., et al., 2013. Industrial land development and manufacturing deconcentration in Greater Jakarta. Urban Geogr. 34(7), 950-971. doi: 10.1080/02723638.2013.783281
[24]	Hudalah, D., 2017. Governing industrial estates on Jakarta’s peri-urban area: From shadow government to network governance. Singap. J. Trop. Geogr. 38(1), 58-74. doi: 10.1111/sjtg.2017.38.issue-1
[25]	Hudalah, D., Nurrahma, V., Sofhani, T.F., et al., 2019. Connecting fragmented enclaves through network? Managing industrial parks in the Jakarta-Bandung urban corridor. Cities. 88, 1-9.
[26]	Hudalah, D., Octifanny, Y., Talitha, T., et al., 2020. From metropolitanization to megaregionalization: intentionality in the urban restructuring of Java’s North Coast, Indonesia. J. Plan. Educ. Res. 44(1), 292-306.
[27]	Jat, M.L., Chakraborty, D., Ladha, J.K., et al., 2020. Conservation agriculture for sustainable intensification in South Asia. Nat. Sustain. 3, 336-343.
[28]	Jatav, S.S., Naik, K., 2023. Measuring the agricultural sustainability of India: An application of pressure-state-response model. Reginal Sustainability. 4(3), 218-234.
[29]	Kasem, S., Thapa, G.B., 2012. Sustainable development policies and achievements in the context of the agriculture sector in Thailand. Sustain. Dev. 20(2), 98-114. doi: 10.1002/sd.v20.2
[30]	Kurnia, A.A., Rustiadi, E., Pravitasari, A.E., 2019. Cluster Analysis and Spatial Pattern Approaches in Identifying Development Pattern of Bodebek Region, West Java. [2023-12-20]. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11372/2541834/Cluster-analysis-and-spatial-pattern-approaches-in-identifying-development-pattern/10.1117/12.2541834.short
[31]	Lankoski, J., Lankoski, L., 2023. Environmental sustainability in agriculture: Identification of bottlenecks. Ecol. Econ. 204, 107656, doi: 10.1016/j.ecolecon.2022.107656.
[32]	Liu, Y., Yang, W., 2019. Leadership and governance tools for village sustainable development in China. Sustainability. 11(20), 5553, doi: 10.3390/su11205553.
[33]	Liu, Y., Zou, L., Wang, Y., 2020. Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years. Land Use Policy. 97, 104794, doi: 10.1016/j.landusepol.2020.104794.
[34]	Mariano, M.J., Villano, R., Fleming, E., 2012. Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines. Agric. Syst. 110, 41-53.
[35]	McGee, T.G., 1991. The Emergence of Desakota Regions in Asia:Expanding a Hypothesis. Honolulu: University of Hawaii Press.
[36]	MoA Ministry of Agriculture, 2019. Rice Fields Distribution in 2019. Jakarta: Ministry of Agriculture (in Indonesian).
[37]	MoEF Ministry of Environment and Forestry, 2019. Forest Status and Water Areas. Jakarta: Ministry of Environment and Forestry (in Indonesian).
[38]	MoEF Ministry of Environment and Forestry, 2020. Land Cover in 2019. Jakarta: Directorate of Forest Resources Inventory and Monitoring, Ministry of Environment and Forestry (in Indonesian).
[39]	MoHA Ministry of Home Affairs, 2019. Population and Civil Registry Data. Jakarta: Ministry of Home Affairs (in Indonesian).
[40]	Müller, B., Hoffmann, F., Heckelei, T., et al., 2020. Modelling food security: Bridging the gap between the micro and the macro scale. Glob. Environ. Chang. 63, 102085, doi: 10.1016/j.gloenvcha.2020.102085.
[41]	Mulya, S.P., 2022a. Determining of peri-urban in rice production centers area: Case study in Karawang Regency, Indonesia. IOP Conference Series: Earth and Environmental Science. 1109, doi: 10.1088/1755-1315/1109/1/012072.
[42]	Mulya, S.P., Munif, M., Pravitasari, A.E., et al., 2022b. Land use and spatial planning in the border area of Bogor Regency and Bogor City, West Java Province, Indonesia. IOP Conference Series: Earth and Environmental Science. 950, doi: 10.1088/1755-1315/950/1/012099.
[43]	Mulya, S.P., Putro, H.P.H., Hudalah, D., 2023. Review of peri-urban agriculture as a regional ecosystem service. Geogr. Sustain. 4(3), 244-254.
[44]	Murakami, A., Medrial Zain, A., Takeuchi, K., et al., 2005. Trends in urbanization and patterns of land use in the Asian mega cities Jakarta, Bangkok, and Metro Manila. Landsc. Urban Plan. 70(3-4), 251-259. doi: 10.1016/j.landurbplan.2003.10.021
[45]	Murtadho, A., Pravitasari, A.E., Munibah, K., et al., 2022. Controlling the urban physical development in Karawang and Purwakarta Regencies using Quantitative Zoning Approach. Indonesian Journal of Geography. 54(2), 272-279.
[46]	Pan, S.Y., He, K.H., Lin, K.T., et al., 2022. Addressing nitrogenous gases from croplands toward low-emission agriculture. npj Clim. Atmos. Sci. 5, 43, doi: 10.1038/s41612-022-00265-3.
[47]	Paz, D.B., Henderson, K., Loreau, M., 2020. Agricultural land use and the sustainability of social-ecological systems. Ecol. Model. 437, 109312, doi: 10.1016/j.ecolmodel.2020.109312.
[48]	Peterman, A., Behrman, J.A., Quisumbing, A.R., 2014. A review of empirical evidence on gender differences in nonland agricultural inputs, technology, and services in developing countries. In: Quisumbing, A., Meinzen-Dick, R., Raney, T., et al. (eds.). Gender in Agriculture. Dordrecht: Springer. doi: 10.1007/978-94-017-8616-4_7.
[49]	Pham, L.V., Smith, C., 2014. Drivers of agricultural sustainability in developing countries: A review. Environ. Syst. Decis. 34, 326-341.
[50]	Pham Thi, N.P., Kappas, M., Faust, H., 2021. Impacts of agricultural land acquisition for urbanization on agricultural activities of affected households: A case study in Huong Thuy Town, Thua Thien Hue Province, Vietnam. Sustainability. 13(15), 8559, doi: 10.3390/su13158559.
[51]	Pravitasari, A.E., Saizen, I., Rustiadi, E., 2016. Towards resilience of Jabodetabek Megacity: Developing local sustainability index with considering local spatial interdependency. Int. J. Sustain. 4(1), 27-34.
[52]	Pravitasari, A.E., Rustiadi, E., Singer, J., et al., 2017. Developing local sustainability index (LSI) at village level in Jambi Province. In:The International Conference of 8^th Rural Research and Planning Group. Yogyakarta, Indonesia.
[53]	Pravitasari, A.E., Rustiadi, E., Mulya, S.P., et al., 2018a. Identifying the driving forces of urban expansion and its environmental impact in Jakarta-Bandung mega urban region. IOP Conference Series: Earth and Environmental Science. 149, doi: 10.1088/1755-1315/149/1/012044.
[54]	Pravitasari, A.E., Rustiadi, E., Pratika Mulya, S., et al., 2018b. Developing regional sustainability index as a new approach for evaluating sustainability performance in Indonesia. Environ. Ecol. Res. 6(3), 157-168.
[55]	Pravitasari, A.E., Rustiadi, E., Singer, J., et al., 2019. Identifying local spatially dependent driving factors of village development in Jambi Province. In:The 9^th International Conference of Rural Research and Planning Group. Bali, Indonesia.
[56]	Pravitasari, A.E., Priatama, R.A., Mulya, S.P., et al., 2022. Local sustainability performance and its spatial interdependency in urbanizing Java Island: The case of Jakarta-Bandung Mega Urban Region. Sustainability. 14(21), 13913, doi: 10.3390/su142113913.
[57]	Pribadi, D.O., Pauleit, S., 2016. Peri-urban agriculture in Jabodetabek Metropolitan Area and its relationship with the urban socioeconomic system. Land Use Pol. 55, 265-274.
[58]	Pribadi, D.O., Rustiadi, E., Panuju, D.R., et al., 2018. Regional Development Planning Modelling: Concept, Method, Application, and Computation Technic (1^st ed.ed.). Bogor: Crestpent Press (in Indonesian).
[59]	Public, Works and Housing, Office, 2013. Karawang Regency Regional Spatial Plan 2011-2031. Karawang: Public Works and Housing Office (in Indonesian).
[60]	Ruiz-Martinez, I., Marraccini, E., Debolini, M., et al., 2015. Indicators of agricultural intensity and intensification: A review of the literature. Ital. J. Agron. 10(2), 74-84.
[61]	Rustiadi, E., Pravitasari, A.E., Setiawan, Y., et al., 2021. Impact of continuous Jakarta megacity urban expansion on the formation of the Jakarta-Bandung conurbation over the rice farm regions. Cities. 111, 103000, doi: 10.1016/j.cities.2020.103000.
[62]	Schreiner, V.C., Link, M., Kunz, S., et al., 2021. Paradise lost? Pesticide pollution in a European region with considerable amount of traditional agriculture. Water Res. 188, 116528, doi: 10.1016/j.watres.2020.116528.
[63]	Shaffril, H.A.M., Krauss, S.E., Samsuddin, S.F., 2018. A systematic review on Asian’s farmers’ adaptation practices towards climate change. Sci. Total Environ. 644, 683-695.
[64]	Sheng, Y., Ding, J., Huang, J., 2019. The relationship between farm size and productivity in agriculture: Evidence from maize production in Northern China. Am. J. Agr. Econ. 101(3), 790-806. doi: 10.1093/ajae/aay104
[65]	Shriar, A.J., 2000. Agricultural intensity and its measurement in frontier regions. Agrofor. Syst. 49, 301-318.
[66]	Skog, K.L., Bjørkhaug, H., 2020. Farmland under urbanization pressure: conversion motivation among Norwegian landowners. Int. J. Agric. Sustain. 18(2), 113-130. doi: 10.1080/14735903.2020.1719774
[67]	Sroka, W., Dudek, M., Wojewodzic, T., et al., 2019. Generational changes in agriculture: The influence of farm characteristics and socio-economic factors. Agriculture. 9(12), 264, doi: 10.3390/agriculture9120264.
[68]	Teillard, F., Allaire, G., Cahuzac, E., et al., 2012. A novel method for mapping agricultural intensity reveals its spatial aggregation: Implications for conservation policies. Agric. Ecosyst. Environ. 149, 135-143.
[69]	Teillard, F., Doyen, L., Dross, C., et al., 2017. Optimal allocations of agricultural intensity reveal win-no loss solutions for food production and biodiversity. Reg. Environ. Change. 17, 1397-1408.
[70]	Tisdale, H., 1942. The process of urbanization. Soc. Forces. 20(3), 311-316. doi: 10.2307/3005615
[71]	Turner, B.L. II, Doolittle, W.E., 1978. The concept and measure of agricultural intensity. The Professional Geographer. 30(3), 297-301. doi: 10.1111/j.0033-0124.1978.00297.x
[72]	Unde, S., Tarde, V., Sonawne, H., 2020. Effect of urbanization on agriculture in peri-urban area of Kolhapur. Gujarat Journal of Extension Education. 31(1), 35-38.
[73]	Vagneron, I., 2007. Economic appraisal of profitability and sustainability of peri-urban agriculture in Bangkok. Ecol. Econ. 61(2-3), 516-529. doi: 10.1016/j.ecolecon.2006.04.006
[74]	Wall, D., 1989. Regional physical planning programme for transmigration (RePPProT). In: Joint Seminar on Watershed Research and Management. Jakarta, Indonisia.
[75]	Widiatmaka, W., Ambarwulan, W., Firmansyah, I., et al., 2014. Land suitability and dynamic system modelling to define priority areas of soybean plantation in paddy fields in Karawang, West Java. Agrivita. J. Agr. Sci. 36(3), 235-248.
[76]	Wu, F., Keil, R., 2020. Changing the geographies of sub/urban theory: Asian perspectives. Urban Geogr. 41(7), 947-953. doi: 10.1080/02723638.2020.1712115
[77]	Wu, J., Bai, Z.K., 2022. Spatial and temporal changes of the ecological footprint of China’s resource-based cities in the process of urbanization. Resour. Policy. 75, 102491, doi: 10.1016/j.resourpol.2021.102491.
[78]	You, H., 2016. Impact of urbanization on pollution-related agricultural input intensity in Hubei, China. Ecol. Indic. 62, 249-258.
[79]	Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., et al., 2021. ESA WorldCover 10 m 2020 v100. [2023-12-20]. https://doi.org/10.5281/zenodo.5571936.
[80]	Zhang, Y.J., Yu, Q., 2020. Identification of current research intensity and influence factors of agricultural nitrogen loss from cropping system. J. Clean Prod. 276, 123308, doi: 10.1016/j.jclepro.2020.123308.

	Variable	Reference
Social aspect	Percentage of people enrolled in formal education per 1000 persons (%)	Pravitasari et al. (2016, 2017)
	Percentage of people enrolled innon-formal education per 1000 persons (%)
	Average distance from village office to education institutions (km)
	Average distance from village office to health facilities (km)
	Total number of health facilities
	Percentage of people suffering from malnutrition in 2019 (%)
	Percentage of people suffering from diseases in 2019 (%)
	Percentage of worship placesper 1000 persons (%)
Economic aspect	Percentage of households using electricity from state-owned electricity companies (%)	Pravitasari et al. (2016, 2017)
	Percentage of households living in slums (%)
	Number of base stations or cell towers
	Number of communication service operators
	Number of small industries per 1000 persons
	Number of cooperatives and banks per 1000 persons
	Number of markets and minimarkets per 1000 persons
	Distance from village office to sub-district general office (km)
	Distance from village office to bank (km)
	Distance from village office to market or minimarket (km)
	Travel time from village office to sub-district office (min)
Environmental aspect	Number of landslide events in a year	Pravitasari et al. (2016, 2017)
	Number of flood events in a year
	Number of flash flood events in a year
	Number of earthquake events in a year
	Number of tornado events in a year
	Number of forest fire events in a year
	Number of drought events in a year

		LoAS
		Extremely low values (EL)	Low values (L)	Medium values (M)	High values (H)	Extremely high values (EH)
VAI	High values (H)	H-EL (LoAS 1)	H-L (LoAS 2)	H-M (LoAS 3)	H-H (LoAS 4)	H-EH (LoAS 5)
	Medium values (M)	M-EL (LoAS 1)	M-L (LoAS 2)	M-M (LoAS 3)	M-H (LoAS 4)	M-EH (LoAS 5)
	Low values (L)	L-EL (LoAS 1)	L-L (LoAS 2)	L-M (LoAS 3)	L-H (LoAS 4)	L-EH (LoAS 5)

LoAS classification	Percentage of agricultural land per village (%)	Description
LoAS 1	0.0	The village area is not planned for agriculture.
LoAS 2	0.1-33.0	A total of 0.1%-33.0% of the village area is planned for agriculture.
LoAS 3	33.1-66.0	A total of 33.1%-66.0% of the village area is planned for agriculture.
LoAS 4	66.1-90.0	A total of 66.1%-90.0% of the village area is planned for agriculture.
LoAS 5	90.1-100.0	A total of 90.1%-100.0% of the village area is planned for agriculture.

Variable in social aspect	Factor 1	Factor 2	Factor 3
Percentage of people enrolled in formal education per 1000 persons	0.680552	0.072883	-0.162538
Percentage of people enrolled in non-formal education per 1000 persons	0.858004	-0.079487	0.094542
Average distance from village office to education institutions	-0.513241	0.015427	-0.594235
Average distance from village office to health facilities	0.032650	-0.006847	-0.899240
Total number of health facilities	0.840806	-0.049193	0.167169
Percentage of people suffering from malnutrition in 2019	-0.046853	0.866815	-0.076713
Percentage of people suffering from diseases in 2019	-0.005478	0.870675	0.071666
Percentage of worship places per 1000 persons	0.759355	-0.045506	0.165533
Explained variance	2.749604	1.525849	1.263472
Proportion of the total variance (%)	36.598130	18.862420	13.776020
Eigenvalue	2.927850	1.508994	1.102081
Cumulative eigenvalue	2.927850	4.436844	5.538925

Variable in economic aspect	Factor 1	Factor 2	Factor 3	Factor 4
Percentage of households using electricity from state-owned electricity companies	-0.019676	0.305219	0.671250	0.150373
Percentage of households living in slums	0.007955	0.256210	-0.711264	0.165064
Number of base stations or cell towers	0.174015	0.840142	0.012593	0.028943
Number of communication service operators	0.031884	0.840611	0.001222	-0.077582
Number of small industries per 1000 persons	-0.735944	0.073430	0.163860	0.086633
Number of cooperatives and banks per 1000 persons	-0.563205	-0.272259	0.011838	0.049155
Number of markets and minimarkets per 1000 persons	-0.039952	0.009062	0.014369	-0.899607
Distance from village office to sub-district general office	-0.630550	-0.027113	0.055335	-0.158234
Distance from village office to bank	-0.636926	0.110621	0.023625	-0.547824
Distance from village office to market or minimarket	0.634535	0.143364	0.233843	0.052561
Travel time from village office to sub-district office	0.514456	0.360418	0.069472	0.269525
Explained variance	2.362724	1.814294	1.046958	1.276485
Proportion of total variance (%)	25.776050	14.738600	9.471530	9.108930
Eigenvalue	2.835365	1.621246	1.041868	1.001982
Cumulative eigenvalue	2.835365	4.456611	5.498480	6.500461