Cost evaluation of multi-sector adaptation pathways:
A case for nature-based adaptation strategies
Jung Hee Hyun1, Dong Kun Lee1, 2, Ji Yeon Kim2, Chae Yeon Park3
1Interdisciplinary Program in Landscape Architecture, Seoul National University, Rep. of Korea. 2Department of Landscape Architecture and Rural System Engineering, Seoul National University, Rep.

of Korea. 3Research Institute of Agriculture and Life Sciences, Seoul National University, Rep. of Korea

1. Research Purpose

2. Innovative Adaptation Planning Frameworks

Climate change already affects local communities, and these impacts are projected to become moresevere and intense in the future (IPCC, 2012). Benefits of implementing climate adaptation at the local
level have been widely recognized with increased numbers of adaptation planning support tools provided
by various actors (ICLEI, 2010; Giordano et al., 2013). However climate change demands a long-term
perspective, traditional values and priorities in planning are challenged, and the responsibility to takeaction is ambiguous. The aim of this study is to first develop a planning model that can determine a paretoof optimal plans to maximize the multi-sector benefits and second to evaluate the costs of plans withnature-based strategies against the alternative.
Rather than fixing the budget constraint or the adaptation goal, this model is able to provide real-time
simulation of optimal plans depending on the user's needs and uncertainties. By developing this model
into a user interface, the usability of this method in planning for adaptation will be evaluated by actual
policy practitioners. Benchmarking this case study and methodology, decision-makers will be able toactively engage in developing their adaptation pathway.

A recent suggested strategy is using the concept of “adaptation pathways(AP)” to systematically and dynamicallysequence adaptation solutions across a long time-frame (Haasnoot et al., 2013; Kwakkel et al., 2016), where eachpathway represents an alternative plan. A sequential approach can realistically consider the short-term constraints
with a goal-oriented long term perspective.
Recent studies on adaptation to climate change are increasing the quantification of additional and functional effectsof various green infrastructure technologies (Mullaney et al., 2015; Zölch et al., 2016). Nature-based adaptation
reduces the impact of climate change by reinforcing natural systems in a concept similar to green infrastructure, andappears to be more environmentally-friendly, sustainable and sometimes more cost-effective than structure-based
adaptation technologies. The nature-based technologies considered in this study are limited to roadside trees, grass
planting and wall greening.
This study differs from past research and traditional planning models in the following aspects:
 Adaptation pathways have not been able to consider the synergies and trade-off of different sectors by incorporating them into a single planning model Method is adjustable to different spatiotemporal scales for future studies

3. Study Scope and Input Data

4. Application of Multi-objective Optimization Method• Seoul, South Korea was selected as a reference site based on the its heat and urban flooding vulnerabilities
 Urban heat island: low rise buildings wide street walks
 Flooding: large watershed (potential for flood based on DEM, drainage line, etc) and past flooding record
 Vulnerable populations – children and elderly• Planning time horizon was set to 2020~2100, to which 8 adaptation technologies were evaluated and future climate impacts
were modeled using HadGEM2 RCP 8.5 scenario by the Korea Meteorological Administration (KMA)• The following inputs were used to search for optimal Adaptation Pathways
Climate Impacts

Evaluation of Adaptation Technologies

Optimization was conducted using a non-dominated sorting algorithm (NSGA-II) whereoptimality of plans were determined by two decision variables – total adaptation effect(reducing flooding and heat-related moralities) and cost. The cost of APs were calculated as
below using NPV.
𝑡𝑑 ,𝐾

NPV𝑎 =𝐶𝑖,𝑘+ ෍
1 + 𝑟 𝑡𝑑

𝑡=1,𝑘=1𝐶𝑡,𝑘1+𝑟 𝑡+𝐶𝑖,𝑘+1 + 𝑟 𝑇−𝑡𝑑

𝑇,𝐾෍
𝑡=𝑡𝑑 ,𝑘=1𝐶𝑡,𝑘1+𝑟 𝑡

Constraints

Heat-related Mortality (#)
160

143

152

10 year budget
constraints andreaching
minimum
impact reduction
targets

129

140

112

120

98

100
80
60

39

40

17
4

20
020-'29

30-'3940-'4950-'5960-'6970-'7980-'8990-'99Source: Kim et al. (2018)

Flood Risk Area from 80mm/hr Rain (km2)
128

140
120

95

100
80

118

66

76
63

74

78

50-'5960-'6960
4020020-'29

30-'3940-'49

70-'7980-'8990-'99Source: Calculated by authors

5. Adaptation Pathway Optimization Results
Adaptation Pathways with Nature-based Strategies

Adaptation Pathways without Nature-based Strategies

Adaptation pathways were optimized for 1000
iterations (results are shown below) and resulted ina pareto of 100 APs as shown on the right. X-axis
indicates the total reduction of flooding induced
damage area (km2) and the y-axis indicates the heat
related mortality. The z-axis is the NPV cost of theentire AP.
The detailed drawing of a sample adaptation
pathway shows the number of each adaptation
technology to be invested each 10 year planninginterval.

Pathway with and without nature-based strategies
showed significant difference in what technologies,
and to what scale, were selected into the plans.
Pathways with nature-based solutions included
fewer and delayed investments of traditional grey
infrastructure technologies.

1000 iterations of optimizing 100 AP sets

1000 iterations of optimizing 100 AP sets

6. Cost Evaluation of Adaptation Pathway with and without Nature-based Strategies
The benefits of nature-based strategies arerealized directly (in terms of cost-benefit) as
they serve as buffers to delay and partiallyreduce the investment of grey infrastructure.
The cost of APs is much more varied for thoseincluding nature-based strategies as a result of
the trade-offs in investing in the alternatives.
In both scenarios a noticeable drop in NPVs
from the pathway is shown around 2030 – thisdrop can be defined as an "investment tipping
point“ where an inefficient investment in timeof the initial strategy, though later in time willreach economic efficiency, occurs. (L.T. deRuig et al., 2019) The two figures ultimately
show that APs were cost-optimized and thatnature-based adaptation strategies are a costeffective alternative to traditional measures

This work is supported by Korea Environment Industry & Technology Institute(KEITI) through Climate Change R&D Program, funded by Korea Ministry of Environment (MOE)(2018001310002)"