## 1 Introduction  
Environmental conflicts, which originate as a result of environmental pollutions, resource use competition, and social conflicts, emerge when stakeholders take part in activities with contradictory interests, values, power, perceptions, and goals. Environmental conflicts cover different issues: Biodiversity conflicts [1; 2; 3], coastal zone conflicts [4; 5; 6; 7], air pollution conflicts [8], land use conflicts [9; 10], and water conflicts [11; 12; 13; 14]. Most recently, environmental conflict is considered in relation to economic,development and social issues in the context of global climate change [15; 16]. Worldwide, environmental conflicts challenge the economic security at both local, regional, national, and global scales [17; 18]. Particularly in coastal zones, environmental conflicts occur as a result of the negative impacts of environmental pollution by sectors and activities. The most sensitive and conflictive sectors are mining, land use, shrimp farming, fossil fuels, biomass, and hydropower plants [18; 19; 20]. In the mineral mining industry, environmental conflicts result from inadequate public information, stereotypes in decision making, the potential of problems to disappear, technical solutions, and archaic techniques [21]. Effective tools such as integrated coastal zone management (ICZM), marine spatial planning (MSP), and multi-planning integration advancing coastal zone management are recommended to address environmental conflicts in coastal areas [22; 23; 24]. The potential of mathematical models in environmental conflict analysis is internationally recognized. The General Algebraic Modelling System (GAMS) is applied to analyze coastal land conflicts [25]. The Analytic Network Process (ANP) is combined with the Driver-Pressure-State-Impact-Response (DPSIR) model to address conflict [26]. Applying multiple-criteria decision-making (MCDM) to environmental decision-making allows defining optimal specifications to be applied to environmental conflicts. For example, the Analytic Hierarchy Process (AHP) is used to evaluate open pit coal production [27]. Intuitionistic fuzzy set (IF) and AHP is combined to select best drilling mud for drilling operations [28]. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied to select best compromise alternatives for water resource use [29]. AHP is integrated with Fuzzy TOPSIS to assess the conservation priority for six alternatives sites of a coastal area [30]. Integrating Delphi and Fuzzy AHP-TOPSIS allow weighting criteria and prioritizing heat stress indices in surface mining. In this case, Delphi extracts criteria based on the advantages of occupational health experts and selected criteria are weighed using the most suitable heat stress index based on Fuzzy TOPSIS [31]. This paper aims applying a hybrid approach using a combination of fuzzy AHP and fuzzy TOPSIS to measure environmental conflicts emerging as a result of titan mining along the Central Coast Vietnam. This area has an abundant potential for mineral mining of titanium and zircon [32]. Four key titan mining sites currently exist in this area: the Ky Khang mine (Ha Tinh province), Nhat Le (Quang Binh), De Gi (Binh Dinh), and Nhum (Binh Thuan) [33]. Titan mining contributes significantly to the local economy; however, it causes environmental problems. Most of titan mining sites are open pits with environmental pollution, degraded mangrove ecosystems, negatively affected human health and local livelihoods. Also, land use and land cover changes (LULCC) are evidenced: Vegetation cover is cutoff, and is replaced by temporary transportation infrastructure for large vehicles such as excavators, containers, and trucks. The environmental problems related to mining activities causing conflicts between stakeholders as well as conflicts between the titan mining industry with agriculture, forestry, infrastructure, and heritage sites are intense and result in major social conflicts.