A Data-driven Analysis to Attest Regional Interlinkages between
Economics and Environment: Utilization of SDG Indicators for Spatiotemporal Analysis
Gigih Fitrianto
Graduate School of Economics
Hiroshima University of Economics
Summary of Dissertation
There is no doubt that one of the major causes of the current environmental changes isdue to human, especially economic, activity. The purpose of this dissertation is toelucidate the temporal and spatial linkages between indicators for the Sustainable
Development Goals (SDG indicators) and to conduct a comprehensive regional analysis.
For this purpose, the availability of data according to the number of spatial and temporal
data is important. This thesis attempts to develop a data-driven analytical approach under
the limitations of data size for Indonesian data.
Chapter 1 provides a framework for data-driven analysis through the use of SDG
indicators, which are designed to address socio-economic and environmental issues in an
integrated manner in order to build a more comprehensive regional development analysis.
In this chapter, we have developed a framework for drawing a general framework between
socioeconomic and environmental factors through spatio-temporal analysis. For
Indonesia's data, especially for the Gross Regional Product (GRP) by industrial sector,
there is a change in the data classification from a base year of 2000 to a data classification
with 2010 as the base year. This is a change in the classification of industries counted in
the GRP data from 9 to 17 industries. With this new classification, data from Statistics
Indonesia will be available from 2010. Thus, the maximum period of systematic
availability is 9 years at the time of writing. The basic previous studies in the time series
analysis in Chapter 2 and the regression model in Chapter 3 are considered in this chapter.
In Chapter 2, we analyzed the economic structural changes and regional resilience ofneighboring municipalities using SDG indicators. A time series analysis of the broken
time trend model was conducted using spatial dependency information. We focused on
detecting the resilience of the regional spectrum to economic shocks and comparing theeconomic growth trends of each region before and after the shocks. The Lehman shock
was chosen as a case study. Since the Lehman shock is the case study, this study does notuse Indonesian data due to data limitations, but compares data from the U.S. and Japan,

where larger data sizes are available: data for 51 U.S. states from 2005:Q1-2018:Q2(number of periods: 54) and data for 47 prefectures of Japan from 2001-2014 (number ofperiods: 14). We extend the broken time trend model to a spatial autoregressive modeland consider several statistical tests for detecting structural changes, such as the F-test
and Wald test. In the U.S., the F-test is shown to be better at detecting spatial clusters
when the spatial factor is not taken into account, but the Wald test is shown to be better
when the spatial factor is taken into account. In Japan, the Wald test is not usable due tothe limited number of data.
In the United States, the negative effects of the crisis are concentrated in the West Coast,Southeast, and Great Lakes regions. In the states in these regions, manufacturing,construction, insurance, and finance are the main contributors to the GRP. On the other
hand, states dependent on agriculture, forestry, fisheries, fishing, hunting and mining weremore resistant to shocks. In Japan, negative impacts were spread across all regions. Afterthe crisis, most of the prefectures recovered, with the exceptions of Nagasaki Prefecture,for example.
Chapter 3 examines the general methodology for variable selection in the spatial
regression analysis to demonstrate the inter-regional linkages between SDG indicators forthe island of Sumatra, Indonesia. This chapter focuses on spatial factors in regional
analysis to elucidate the interlinkages between SDG indicators across the region. As not
all information is reliable to explain the data, a variable selection process was used toselect the best variables to prove the interlinkages in and between the provinces.
In order to obtain the best variables to prove interregional linkages, we used a linear
transformation of the full spatial panel model, the largest model used in spatial panel
analysis, to obtain the best variables to prove the linkages. Variable Selection in SpatialRegression (VSSR) is newly developed and used.
The full spatial panel model can be expressed as follows,𝒚 = α𝟏 + δ𝐖𝒚 + 𝐗 𝛃 + 𝐖𝑿𝒕 𝛉 + 𝝂𝝂 = λ𝐖𝝂 + 𝝐 ,𝝐 ∼ i. i. d. N(0, 𝜎 𝐈), 𝑡 = 1, … , 𝑇(1)
where 𝒚 gives 𝑁 × 1 vector of dependent variable at time 𝑡 with 𝑁 being thenumber of regions. 𝐗 = [𝑥 , … , 𝑥 ] is 𝑁 × 𝐾 matrix which consists of 𝐾 numberof independent variables. The 𝐖 is a spatial neighbor matrix of size 𝑁 × 𝑁 with 𝑤 =0 , which stores the neighborhood information between regions. The α represents a

common intercept and 𝟏 = [1 , … , 1] is 𝑁 × 1 vector of one. 𝛃 and 𝛉 are
𝐾 × 1 vectors of regression coefficients and spatial regression coefficients forexplanatory variables respectively. To obtain the best variables to attest regionalinterlinkage, VSSR used a linearized transformation of equation (1), into(𝐈 − λ𝐖)𝒚= (𝐈 − λ𝐖){α𝟏 + δ𝐖𝒚 + 𝐗 𝛃 + 𝐖𝑿𝒕 𝛉} + 𝝐𝒚 = 𝛼 𝟏 + 𝛿𝑾𝒚 + 𝑿 𝜷 + 𝑾 𝑿𝒕 𝛉 + 𝝐(2)The VSSR can be obtained from following procedures,
Step 0. Use the linearized form in equation (2) as base model.
Step 1. Fix λ in the interval [0, 𝜙) for grid search, where 𝜙 is the reciprocal of themaximum eigen value of matrix 𝐖.
Step 2. Obtain the least square estimates 𝛼 (𝜆), 𝛿 (𝜆), 𝜷(𝜆), 𝛉(𝜆) and σ (𝜆) for fix 𝜆.
Step 3. Find the best model minimizing AIC(𝜆) for variable selection based onfollowing calculation,AIC(𝜆) = 𝑁𝑇 log(2𝜋𝑒) + 𝑁𝑇 log σ (𝜆) + 2 ∙ 𝑇 log|𝐈 − 𝜆𝐖| + 2 ∙ 𝑑where 𝑁 is the number of province in Sumatra island, 𝑇 is number of time
period, and 𝑑 is the number of parameters for 𝛼 , 𝛿, 𝜎 , 𝛃 and 𝛉.
The variable selection was conducted by evaluates the model by dropping one
explanatory variable at a time, which minimizes the AIC value, until there is no
significant drop in AIC value. In this study we used step function in R library.
Step 4. Repeat Steps 1 – 3.
In this empirical analysis, we apply the VSSR to observe the relationship between
industry and regional income, mainly palm oil, in Sumatra, Indonesia. Using annual datafrom 2011 to 2017 for 10 provinces in Sumatra, the dependent variable is regional income,and the independent variables are 1) GRP growth rate per capita, 2) secondary school
enrollment rate, 3) population growth rate, 4) crude palm oil production per labor force,5) mining production per labor force, and 6) wholesale production per labor force.
quantity, and 7) the ratio of children under fifteen years old to population (child laborratio). The results show that school attendance, palm oil production, and wholesale tradewere selected and positively influenced the local income. On the other hand, mining andchild labor rates are negatively related after the selection of VSSR. Neighborhood schools,
palm oil production, and wholesale trade had a positive impact on each province inSumatra. Neighborhood income and child labor rates in neighboring provinces have anegative effect. This chapter further explains how to incorporate data between socioeconomic variables (e.g., income inequality, child labor participation) and environmental

data (e.g., change in forest cover, CO2 emissions, and forest fire emissions), and thenexamines the actual addition of the standard deviation (degree of topographicunevenness) of the aggregated 1 km square elevation of each province in Sumatra as avariable. The results showed that states with more plains had significantly higher regional
income. This was suggested to be related to the fact that the suitable land for palm oilplantations is flat land.
In Chapter 4, the giant grid space adjacency matrix of socioeconomic grid cell data forcomputationally efficient and sustainable economic analysis was formulated. The
advantage of using grid cell data for socio-economic analysis is that it is relatively easy
to incorporate satellite data into regional analysis and has an important role to play inobserving the relationship between the socio-economy and the environment. This
corresponds to the sustainable development goals of harmonizing socio-economic andenvironmental balances. The creation of a spatial adjacency matrix to project spatial
relationships within a region plays an important role in such an analysis. However, noprevious studies have provided a practical formulation of spatial adjacency matrices ingrid cell data structures that take into account socioeconomic analysis. This problem
stems from the existence of NA cells representing non-inhabited areas, such as water
bodies, but the shapefiles commonly used in spatial analysis do not contain thisinformation within municipalities. Since these NA cells create a non-rectangular grid, it
is important to exclude them in the analysis in order to project the actual information
correctly. In this chapter, a method is provided to construct the adjacency matrix using
Kronecker products and apply a projection matrix to remove the NA cells to project thereal information correctly. The method using the Kronecker product showed very highefficiency compared to the widely used R package called spdep. The experimental results
show that the computation time is more than 2000 times faster than that of the spdep
package for huge data with as many as one trillion elements.
This dissertation has established demonstrative original methodology for elucidating theinteraction between the economy and the environment in regional analysis.