Policy Analysis of Coastal-Based Special Economic Zone Development Using System Dynamics

Special Economic Zone (SEZ) development is becoming a preferable policy by the Indonesian government to boost economic growth in less-developed local regions. This is because of the promise that SEZ could attract investment and job creation based on local competitive commodities. One of these areas is Bitung SEZ, North Sulawesi – Indonesia, a coastal-based SEZ, as its strategic position for logistics, fishery resources, and coconut plantation. To explore the promise of growth proposed by developing SEZ in Bitung, we developed a Systems Dynamics model of the interaction between economic growth, social development, and environmental impacts. Based on the model understanding and development, we identified three factors the Indonesian government should improve: coconut plantation productivity, fisheries ship management, and education index. With these three factors in mind, several policy options were tested in the model, resulting in a more substantial impact than the business-as-usual condition.

Special Economic Zone (SEZ); System dynamics; Policy analysis

As the world’s biggest archipelago nation, Indonesia faces unique challenges due to its unique geographical setting. Those challenges are uneven interregional infrastructure in connectivity (logistics/ transport) and energy domains (Kemenkeu RI, 2017). Under such conditions, aggressive investment plans positively affect job creation (The United Nations Development Programme, 2016). The Government must formulate policies that promote economic development across all regions of Indonesia, aiming to contribute to the Gross Domestic Product (GDP) more evenly and reducing over-reliance on Java Island's growth. SEZs are among the policies that countries employ to stimulate trade, attract investments, and foster overall economic development (ASEAN, 2016). SEZ development will encourage economic growth in its region and distribute the economy evenly (Farole and Akinci, 2011). It can create technological improvements to increase national productivity and structural transformation in local areas (The Asian Development Bank, 2015).

Bitung is a district not far from Manado, the capital of the North Sulawesi province. It lies on the northern tip of Sulawesi Island. Given its proximity to the island of Papua, the Northern Moluccas, and the Southern Moluccas, Bitung is a good hub for the northeastern region of Indonesia (Moeis et al., 2021).

Being the promising hub of the northeastern region of Indonesia, it goes without saying that Bitung is considered the potential primary motor of economic growth in that region. With its coastal characteristics, the development of Bitung SEZ (KKPIP, 2015) will attract more economic activities in that region and create the much-needed cargo that will be transported to the western part of Indonesia (Moeis et al., 2017).

Bitung SEZ is one of the coastal-based Special Economic Zones (SEZs) that is vital in supporting Indonesia's maritime fulcrum program. This SEZ focuses on various potential industries, including fishery processing, coconut processing, pharmaceuticals, and electricity production (specifically for smelters), with logistics as supporting initiatives (BPS Kota Bitung, 2017). They are placed in the northern part of the border with the Pacific region, an essential point of the world trade era in the future.

The primary industry of Bitung SEZ is a fishery industry that utilizes marine resources; its management should apply the principle of sustainability that should consider economic, social, and environmental aspects. Therefore, with various factors that influence and be influenced by SEZ development, an analysis is needed to review the sustainability of Bitung SEZ development. SEZ development is a complex problem since it involves multiple stakeholders, has a long-term time dimension, and can be considered ill-structured. Those three characteristics are commonly answered using System Thinking and System Dynamics as the primary (modeling) approach. (Moeis et al., 2020a; Destyanto, Hidayatno, and Amalia, 2017; Hidayatno, Rahman, and Muliadi, 2015; Yuliawati et al., 2015).

The purposes of this research are (1) to understand a coastal-based SEZ using Bitung SEZ as an example and (2) to develop a System Dynamics model that explains the structure of a coastal-based SEZ and assess policy options that can be implemented within it. Thus, the central question of this research is,” How does coastal-based SEZ policy affect the factors that could boost and support sustainable coastal-based SEZ development?”

Methodology: System Dynamics Modeling

2.1. System Diagram

    System Diagram (Frantzeskaki and Walker, 2013) is a bare canvas for analysts to draw a conceptual model of a system. It was derived from the cybernetics framework. It consists of endogenous variables (the system and indicators) and exogenous variables (external factors and policy options). System Diagrams are widely used in policy modeling and analysis. Our (conceptual) model enriches the number of research/ studies that used the Frantzeskaki and Walker (2013) approach.

   SEZs serve as one of the potential solutions to address the economic disparity between the eastern and western parts of Indonesia. By developing SEZs, goods will then be produced, thus lowering the trade imbalance between the western and the eastern regions. The Bitung SEZ System (Figure 1) was divided into economic, social, and environmental loops. Policy Options represent the policy measures that the problem owner (i.e., the Indonesian government) can take to alter the outcome of their interest (GDRP growth, employment rate, ocean acidification). External Factors represent the exogenous variables that the problem owner cannot control. The economic sector comprises a public (government) and private (per household) economy. Employment variables and innovation and education represent social items. The environmental section deals with how industries need natural resources as raw materials and how industrial emissions affect ocean acidification.

   Each loop represents the connection between variables. There are six reinforcing loops and one balancing loop. The first reinforcing loop means private economics that Gross Domestic Regional Product (GDRP) will affect household income. As income increases, the investment amount will also be increased. Therefore, a higher number of investments will contribute positively to GDRP. The second reinforcing loop represents public economics, where GDRP affects household income and consumption, leading to government revenues (Moeis et al., 2020b).

   The third reinforcing loop represents the social aspect, which shows the same GDRP to government expenditure flow that led to the innovation and education index. A good index will lower the unemployment rate. A lower unemployment rate will increase industrial production and GDRP. The fourth reinforcing loop represents fishery, where a higher GDRP will lead to higher technology for the ships to catch fish and increase output and GDP.

      Furthermore, the fifth reinforcing loop represents ocean acidification that will decrease because of technology. Nevertheless, the higher acidification affects the lower number of fish, leading proportionally to industry and GDRP. The sixth reinforcing loop represents coconut plantations that start from GDRP to investment, leading to a more considerable land plantation and coconut. Therefore, increased coconut production may lead to higher production and GDRP. The only balancing loop represents carbon impact from production to ocean acidification, affecting fewer fish and reducing production.

Figure 1 Bitung SEZ system diagram

2.2. Stock and Flow Diagram

        The model has three main modules: the economy module, the social module, the environmental module, and its submodules.

2.2.1. The Economy Module

        The economic module models the impact of a special economic zone on economic variables, both at the macro-state and micro-level of the industries. It calculates the money flow from investments and capital, contributing to valuable production that enhances the GDRP. This module is divided into the Local Economic and the Industrial sub-modules.

2.2.1.1. The Local Economy Submodule

       The main stock in this sub-module is GDRP, with calculations based on expenditure, as shown in Figure 2. In calculating the GDRP of the following year, the aspect of Bitung's Special Economic Zone is combined using increments. Thus, two factors affect the magnitude of GDRP: the natural growth of GDRP, which influenced the amount of GDRP the previous year (which is the value of the stock), and the amount of GDRP from Bitung SEZ. Investments are coming from the government and the private sector. It matches the existing investment projections. They are then divided into investments for innovation facilities, fish processing, and coconut processing.

2.2.1.2. The Industry Submodule

       Figure 3 shows two industries as the focus in the SEZ, namely the processing of coconut and fish. This sub-module starts with an allocated investment. The incoming investment will be processed by giving components such as raw materials and labor next year. The composition of capital for raw materials is quite large, with a percentage of over 60%. Furthermore, this investment can be calculated relative to the initial investment so that the effect can be multiplied by the amount of initial production. The result of production is assumed to be entirely exported as designed in the Master Plan. We also measure the environmental impacts on the industry’s energy use and emissions.

Figure 2 Stock and flow diagram of the local economy submodule

Figure 3 Stock and flow diagram of the industry submodule

2.2.2. The Social Module

        The social module consists of the labor and the innovation & education sub-module. The basic assumption is that innovation and education will enhance labor quality, and good labor quality will also support innovation growth and better education standards.

2.2.2.1. The Labor Submodule

        In the labor submodule, another influential sub-module variable is the allocation of capital for labor costs (the exact number of laborers that can be employed by considering the cost of workers per person). The workforce calculated as part of SEZ is sourced from industry, innovation, and education facilities. With the increased demand for such a workforce, there will be a corresponding rise in the number of people employed, thereby directly reducing the unemployment rate in the SEZ, as shown in Figure 4.

Figure 4 Stock and flow diagram of the labor submodule

2.2.2.2. The Innovation and Education Submodule

       The two essential variables are modeled in this sub-module shown in Figure 5: the education and innovation index. The education index is calculated from the number of students considered in the labor force divided by the number of students. Junior High School, Senior High School, Higher Education, and Elementary students influence the Education Index. The Innovation Index is controlled by the New Innovation Facility and Tech Advance Parameter, respectively.

        The Master Plan of Bitung SEZ has allocated a particular area to build educational facilities, so this development is part of the initial investment. In addition, Bitung SEZ also set up several places to build research facilities to generate innovations that would benefit industry players within the SEZ. The initial assumption is that Bitung  SEZ   has two research facilities for the coconut and fishery industries. This research facility's development level will affect technological progress, ultimately affecting the innovation index, which can be a multiplier factor for industrial productivity improvements.

Figure 5 Stock and flow diagram of the innovation and education submodule

2.2.3. The Environment Module

       According to various studies, environmental factors are sometimes barriers to a rapidly growing industry due to limited resources and how the industry’s output disturbs ecological stability. This module comprises the coconut, the fisheries, and the ocean acidification sub-modules.

2.2.3.1. The Coconut Submodule

       Figure 6 shows the modeling of coconut plantations and their potential to be the industry with the highest added value compared to other coconut products. This coconut potential is seen first from land use for coconut plantations that produce coconuts for raw materials. There may be a difference between the harvested coconut and the processed coconut. Thus, the existence of the ’coconut to or from outer Bitung’ variable becomes a variable which, if positive, means that coconut production exceeds the demand for raw materials for the processing plant. The assumption is that raw materials are often purchased from outside Bitung (only 60% are supplied from within).

Figure 6 Stock and flow diagram of coconut submodule

2.2.3.2. The Fisheries Submodule

       Figure 7 shows that the potential of fish influences the number of fish caught in the catchment area. Moreover, the model shows the relationship between plankton and the number of fish catches. The comparison between plankton and big pelagic fish that became the seed of Bitung is 1 : 10000 (Sverdrup, Duxbury, and Duxbury, 2006).

       The number of plankton, ships, capacity, and average travel also affect fish catches. Like the coconut submodule, there is a possibility that the production of fisheries in Bitung will be surplus. Otherwise, there is a 50% chance that raw materials will not be met if purchased from another region.

Figure 7 Stock and flow diagram of the fisheries submodule

2.2.3.3. The Ocean Acidification Submodule

       In Figure 8, the real impact of SEZ is the electricity consumption that leads to CO₂ emissions expenditure. The CO₂ is then absorbed into the oceans by about 30% of the total produced (Millero, 1995). The CO₂ gets denser in the seawater solution with the absorption, thus affecting the CO₂ pressure. The present ocean conditions around the Pacific Ocean are 8.1 pH, indicating 511 µatm pCO₂. By calculating the pCO₂, it becomes possible to map the effect of ocean acidification by considering the natural increase predicted by experts, which suggests that the ocean's acidity will decrease and become more acidic by 0.4 by the year 2100 (Caldeira and Wickett, 2003). The current level of ocean acidification will affect the level of defense of phytoplankton, the most diminutive living creature in the sea. The number of phytoplankton was measured by multiplication of the area of capture multiplied by the amount of phytoplankton in 1 hectare, which is 1 million cells per mL weighing 14 femtograms per cell (Mora et al., 2013).

Figure 8 Stock and flow diagram of the ocean acidification submodule

2.3. Model Validation

2.3.1. Sensitivity Analysis

        In this simulation, the tested variable was the effect of the employment-technological relative variable on the production value of coconut processing. This relationship is considered valid if it is proved that a higher level of technology in employment and industry will lead to higher industrial production value. Figure 9 shows that both variables show consistent trends.