Port Sustainability Index: Methodological Issues

Introduction

Sustainability in ports is a growing concern for port authorities, port users, policy makers and also local communities [1]. Various activities in ports relating to handling of cargo and ships, logistics and distribution, etc. generate environmental degradation primarily through Conceptual Paper use of fuels and energy [2]. While chemical substances like PMx, Sulphur oxides, Nitrogen Oxides cause harmful effects on the environment (including human health), emissions of greenhouse gases CO2, CH4, N2O, HFCs, PFCs and SF6 (as per Kyoto Protocol in 1997) contribute to global warming and climate change. Harmful effects get extended to oceans and seas and affect marine ecosystems [3]. Shipping affects marine environment also by discharging Ballast water [4], containing a variety of non-native, nuisance, invasive, exotic species that can cause significant ecological and economic damage to aquatic ecosystems along with serious human health problems. Sources of emission at ports could be due to stationary sources like warehouses, mechanical plants, offices, etc. and mobile sources such as ships (commercial and also port- owned), cranes, vehicles, etc. [5]. Even, aerial drones are being used for detecting emissions in ports [6]. However, emissions of pollutants depend on a large number of factors. For example, emission from a ship depends on phase of activities, time taken for each activity, engine type and engine category (main or auxiliary), fuel type, engine nominal power, engine load factors, emission factor for the type of vessel and the pollutant, etc. International shipping contributes around 15% of NOx and 5-8% of SOx emissions worldwide [7] causing serious harm to the environment and human health. As per Brandt, et al. [8] emissions from shipping caused about 50,000 premature deaths in Europe alone in 2000. In addition, oil spills and water pollution from ballast water carrying microorganisms causing significant devastation of local marine species adds to environmental degradation. Green port covers broader topics of ecosystem protection through port sustainability plans and regulations on environmental planning [9]. Liquefied natural gas (LNG) is a promising alternative fuel for shipping as it produces no SOx or PM emissions and much lower NOx. But, the fossil fuel LNG reduces GHG emissions by 25% which is not adequate as per the recent international regulations. Besides alternative fuels, other strategies to reduce emissions include slow steaming, improved hull design, onshore power supply (OPS) or shore-side electricity (SSE). Reduction of speed by 20% helps to reduce fuel consumption by around 40% and CO2 emission by about 7% [10]. Improved designs of hull have been driven by IMO directives like EEDI, SEEMP, etc. [11, 12]. OPS/SSE helps ships not to run their auxiliary generators to provide power.

International Maritime Organization (IMO) has revised the target of GHG emission to be reduced by 70% or beyond and CO2 emission should be close to zero (net) in 2050 from international shipping [13]. Similarly, upper limit of the sulphur content of ships’ fuel oil was reduced to 0.5% (from 3.5% previously) to reduce significantly the amount of sulphur oxide emanating from ships. Assessment of port environmental externalities, and addressing societal needs, economic growth etc. are all included in multidimensional port sustainability index [14].

Roles played by ports are important to the transition of the maritime sector towards sustainability [15]. Sustainable ports focusing on the social, economic and environmental impacts, attempt to mitigate the above effects and also their carbon footprints by adopting policies to comply with national and international regulations [16]. Practices in making green ports involve several dimensions or sub- indices like environmental, technologies, monitoring and upgrading, process and quality improvement, active participation, communication and cooperation along with port management tools, monitoring, etc. [17].

Sustainability issues of port are multi-dimensional. Attempts have been made to construct Composite index (CI) by aggregating all relevant dimensions of sustainability and set of measurable indicators under each dimension to assess overall status facilitating monitoring various aspects of sustainability, comparing and ranking the entities across time and space based on longitudinal and also snap-shot data for better decision making [18].

**Literature Survey**

Stankovic, et al. [21] considered three pillars of sustainable developments of port regions viz. economic growth (10 indicators), social dimension (27 indicators) and environmental dimension (1 indicator viz. 2.5 emission). For Environmental issues and monitoring, Puig, et al. [22] investigated trends of environmental management in European ports and suggested large number of indicators relating to air quality, carbon footprint, energy consumption, marine ecosystems, noise, sediment quality, soil quality, terrestrial habitats, waste management, water quality, etc.

Different CIs and plans with different set of indicators have emerged like Green port program [1], Port Energy Environmental Plan [23], Plans for environmental protection, climate protection, climate initiative [9], clean air plans [24]. Most of large hub ports and many other ports are currently certified to environmental management standards such as ISO 14001.

Environmental standards are not uniform across ports and neither the relationships of environmental efficiency with operational practices of a port system. Port performance measures attempt to meet expectations of customers and stakeholders and consider indicators like cargo throughput, dwell time of vessels at port i.e. turn-around time (TAT), productivity, operating surplus, etc. Usual performance metrics of ports do not integrate sustainability measures like emission levels and effect of energy consumed.

Ports with draft constraints do not allow ships with full shipload of cargo which results in dead freighting, lower TAT, but higher emission per ton of cargo [25]. In addition, dead freighting gives rise to increased number of ship calls which also increases the emission levels. Similarly, old and inefficient cargo handling equipment increases consumption of energy. Port inefficiencies are reflected by longer dwell time of cargo and ships, interruptions in vessel traffic clearance, protracted documentation handling, lesser handling of container per crane-hour, higher emission of GHG gases per ton of cargo, etc. [26]. Volume of CO₂ emission per tonne-km tends to decrease as size of the ships increase [27].

Environmental Port Index (EPI), a shareholding company of ports and municipalities operating ports is located at Port of Bergen, Norway finds a ship’s maximum tolerable environmental impact while at port, in terms of factors which can influence emission of CO₂, SO₂, NOₓ and particle levels (baseline data). Actual data obtained from crew member of a ship on fuel consumption, emission levels, power usages, etc. during the ship’s time at port are compared with the ship’s EPI Baseline and EPI score between 0 and 100 is calculated and informed to the Port Operators, Ship Owners, against fees depending on the GRT. However, the methodology gets changed with every version of the EPI. Thus, EPI scores are not comparable and cannot be used as a time series.

In the context of sustainable development in ports, researchers have discussed impact of maritime transport on environmental performance either for selected performance indicators or through models. Carrera-Gómez, et al. [28] adopted ecological footprint of ports enabling authorities to prepare sustainable development plans by developing footprint-free products and absorption of wastes. European maritime companies used Green Marine Environmental Program (GMEP) to assess improvements of specific environmental performance indicators to maintain certification, where subjective self-evaluations are done to rank the performance indicators on a scale from 1 to 5 [29].

For energy management, studies mostly consider energy scheduling or saving methodologies with emphasis on the reefer clusters. Such optimization approaches are difficult to implement. Chen, et al. [30] described mathematical models to estimate relationship between direct and indirect emissions of GHG from shipping and development of global maritime fleets, in terms of deadweight tonnage (DWD) and found that slowdown of navigation speed, implementation of the Energy Efficiency Design Index (EEDI) and Energy Efficiency Operation Index (EEOI) are effective on the whole. EEDI developed by IMO with the objective of reducing CO₂ emissions as the first step towards shipping decarbonization. EEDI considers mechanical parameters in ratio scales which can influence CO₂ emissions. IRENA [31] suggested computation of EEDI as:

$$\frac{EEDI}{Engine power*Specific fuel consumption*Carbon factor}$$

To calculate CO₂ emissions from equipment and machines within the port terminal, Martinez Moya, et al. [32] suggested computing total CO₂ emissions (in tonnes) at a terminal as

$$CO_2 \text{ emission} = \sum_{i=1}^{4} \left( a_i * f_i \right) + \sum_{j=1}^{4} \left( b_j * f_e \right)$$

Where $a_i$: Yearly consumption of fuel in Tonnes of Oil Equivalents (TOEs) by the $i$-th equipment.

$f_f$: Emission factor in tonnes of CO$_2$ emission per TOE
$b_f$: Yearly consumption of electricity in kWh with j-th equipment
$f_e$: Emission factor in tonnes of CO$_2$ emission per kWh
However, EEDI does not consider operational or commercial aspects. EEDI as not working tool for decarbonisation of shipping [33]. This led to the refinement of the index.

Osipova and Carraro [34] showed limitations of existing regulations in terms of CO$_2$ emissions and recommended for 100% reduction in CO$_2$ emissions by ships at-berth. The European Union (EU) has recently adopted two regulations: the FuelEU Maritime Regulation and the Alternative Fuels Infrastructure Regulation (AFIR). As per the FuelEU Maritime regulation, container and passenger ships, cruise ships) ≥5,000 gross tonnage (GT) must connect to shore power in main EU ports in the trans-European transport network (TEN-T) from 1st January, 2030. The AFIR aims to regulate shore power supply and incentivize infrastructure development in TEN-T ports.

Mallouppas and Yfantis [35] reviewed various pathways and possible technologies to achieve the IMO’s deep decarbonization targets 2050 by the shipping sector and concluded that achievement of IMO’s 2050 targets may be feasible via radical technology shift together with the aid of social pressure, financial incentives, regulatory and legislative reforms at the local, regional and international level given the maritime sector’s 3% contribution to GHG emissions [36].

Besikci, et al. [37] considered Ship Energy Efficiency Management Plan (SEEMP) for existing ships and Energy Efficiency Design Index (EEDI) for new ships in the context of reduction of CO$_2$. For bulk carriers, Fan, et al. [38] built an energy efficiency model based on the Energy Efficiency Operational Indicator (EEOI). While SEEMP attempts to improve energy efficiency of a ship by providing an ongoing indication of CO$_2$ emissions, EEOI examines ship fuel consumption, ship main engine power, and ship drag characteristics. As per Fan, et al. [38], EEOI model provides more accuracy to simulate ship energy efficiency considering cargo load, ship speed, and random effects of natural environmental factors like wind, current, waves, and waterway depth, etc. and facilitates decision regarding optimization of ship energy efficiency. Energy Efficiency Existing Ship Index (EEXI) is another measure, based on the calculation formulas for EEDI establishing legally binding CO$_2$ targets for newly built ships projected to be ratified in 2023, in-line with decarbonization targets in which IMO has planned a 70% reduction in emissions level by 2050 using the same 2008 baseline. Formulation of EEXI and verification by sea trial tests specific to IMO Resolution MEPC. 203 (62), must address SEEMP or EEOI’s shipping practice specific to real load control and management at the operational level. To formulate EEXI, the attained EEDI calculation, i.e., based on theoretical estimations and verified by sea trial tests specific to IMO Resolution MEPC.203 (62), must address SEEMP or EEOI’s shipping practice specific to real load control and management at the operational level satisfying the equation [39]: attained

EEDI = 0.75 x MCR x fuel x CF / DWT / S (g (CO$_2$) / tnm) (3)

Where: 0.75 x MCR = 75% engine load; fuel = fuel consumption; CF = coefficient of CO$_2$ emission (kg/t of fuel); DWT = DWT by 70% payload; and S = ship speed.

However, no study confirms the best method for assessing hazard, risk, and energy assessment [40]. Multicriteria decision-making (MCDM) methods decide weights by subjective, objective or mixed methods. Based on analytic hierarchy process (AHP), Kegalj, et al. [41] came up with a composite environmental index $I_E$ considering environmental indicators like emission of air and noise, waste, energy consumption, water quality, etc. excluding impact of ships at berth and transport of containers from the terminal by rail-road modes. The selected indicators were ranked subjectively by experts on a 5-point scale (1 to 5). Relative weight for each individual indicator was taken in relation to other indicators.

To make the indicators unit-free, Min-Max transformation was used for normalization. $I_E$ was obtained as a weighted sum. Methodological issues of $I_E$ are:

• Problem to find weights of an indicator based on $\frac{n(n-1)}{2}$ pair-wise comparisons for n-number of alternatives in AHP get increased with increase in n. A number of methods proposed to avoid this disadvantage of AHP [42, 43]. But these methods have been criticized as complex, ad-hoc in nature, may not provide efficient way for managerial decision-making in case of high number of alternatives [44].
• Determination of preference for the most important indicator (or the least important indicator), using a scale from 1 to 5 is subjective and is sensitive to the sample composition. Moreover, preferences or judgments of Government and Regularity Authorities may differ significantly from the shippers, port users, etc.
• Scale from 1 to 5 is not equidistant. Construct-distance between 1 and 2 is ≠ construct-distance between 4 and 5 [45]. Hence, addition of preference (ordinal data) is not justified. Hand [46] opined that $X > Y$ or $X < Y$ is meaningless since the arithmetic mean is not defined for ordinal scales.
• $I_E$ as weighted sum does not address variance of the weighted sum and correlation of $I_E$ with the chosen indicators. No weighting system is beyond criticism [47].

- Normalization by Min.-Max function as $z = \frac{X_X}{X_X + X_Y}$ suffers from limitations. Such Z-score of a particular activity is relative to performance of others. Min-Max function changes distribution of the transformed scores and may affect $I_E$ in unknown fashion. It depends on the extreme values which may be unreliable outliers. Gain in Z due to unit increase in X is different for different values of X.

Models based on Data Envelopment Analysis (DEA) were used to estimate environmental and operational performance of ports [25, 48]. However, factors like selection of variables, methods used, associated assumptions, sample size, etc. may distort estimation of port efficiency by DEA [49]. Efficiency values were different for DEA-CCR and DEA-BCC models [50]. Cullinane, et al. [51] observed decreasing return of scale for British ports against increasing returns to scale for Spanish ports [52]. Moreover, the homogeneity condition of DEA may not always be fulfilled. Simple models to estimate CO$_2$ emissions as function of equipment type and transport modes at container terminals were developed [53]. Martinez-Moja, et al. [32] suggested model to evaluate energy efficiency and CO$_2$ emissions of container terminal equipment and found that major sources of CO$_2$ emissions are yard terminal tractors and rubber-tyred gantry cranes (RTGs). Sim [54] calculated total CO$_2$ emission (kg/TEU) at a container terminal as sum of carbon emissions from ship’s movements inside the port, ship at berth, loading/unloading of TEUs, container transportation and container receiving and delivery. But, CO$_2$ emissions from different activities may not follow similar distributions and thus, addition may not be meaningful. $X+Y=Z$ is most meaningful when X and Y follow similar distributions and enable finding distribution of Z i.e.

$$\text{Prob.} (Z \leq z) = \text{Prob.} (X+Y \leq z) = \int_{-\infty}^{\infty} \int_{-\infty}^{-\infty} f_{X,Y}(x,t-x) \, dt$$ (4)

Equation (4) ensures meaningful arithmetic aggregation for computation of mean, variance etc. and undertaking parametric statistical analysis like Principal Component analysis (PCA), Factor analysis (FA), Analysis of variance (ANOVA), statistical inferences like estimation and testing hypothesis of equality of mean across time and space, where basic assumption is normally distributed variables. Regression equation also requires normal distribution of residuals (error in prediction of dependent variable from the independent variable(s)). For normally distributed variable $X$, true CO$_2$ emissions from an activity with $X=x_0$ can be estimated by $x_0 \pm \text{SEM}$ where SEM=Sample $S_E$ [55].

**Discussion**

The proposed method of obtaining normally distributed PSI-scores is simple and easy to comprehend for meaningful evaluation of measurement properties for each indicator and dimension. The method can include all measurable sustainability related indicators (managerial, technological, organizational, and operational) either in ratio scale or in ordinal scale without any bias for developed or undeveloped ports.

Normally distributed PSI satisfies desired properties and basic assumption of statistical techniques and inferences and facilitates better ranking, comparisons across time and space.

Additional benefits include assessment of progress/deterioration of one or a group of ports for monitoring of policies and strategies. Regression equation of PSI on port operations can be fitted using port performance scores. Regression equation of the form Overall Port performance $\alpha + \beta.PSI$ can also be fitted to know effect of PSI on port performance. However, checking normality of error scores is suggested for fitting of regression equations.

It may be argued that the outliers may provide valuable information about port processes and should not be ignored. But outliers are different from mode of the distribution and may also result from measurement errors, data entry mistakes or natural data variation. Outliers can significantly influence values of mean and increase variance of data. In the context of CI, the term “robustness” refers to the handling of outliers and possible small variations in the input parameters [57]. Scatter plots of bivariate data can help in visualizing outliers and omission of them gives better fit of regression equation. An easy way to identify the outliers is through inter-quartile range (IQR) defined as $(Q_1 - Q_2)$ where High outlier $\leq Q_1 + (1.5*IQR)$ and Low outlier $\leq Q_1 - (1.5*IQR)$. Machine learning models and model techniques can be improved by eliminating the outliers [58].

**Conclusion**

The paper suggests a simple and methodologically sound method of obtaining PSI value for a port considering multi-criteria goals including environmental aspects of port’s operations. The index PSI with continuous, monotonic scores follows normal distribution which solves the problems related to skew and outliers within environmental data sets in the port sector and satisfies many desired properties. The method helps the port planners to know overall performance of ports from the sustainability angle along with performances in the relevant dimensions and take necessary actions to balance emission reduction efforts without disturbing international trade and economic growth. Quantification of responsiveness of PSI using longitudinal data helps to assess effectiveness of adopted action plans.

The proposed method avoids disadvantages of existing methods which are either not methodologically sound or involve assumptions, verification of which are required before application of the methods. The method helps to find the growth curve of PSI of a port, which in turn provides another criterion for comparison among ports. The proposed method with wide application areas and benefits advances scholarly. However, the method requires careful selection of dimensions and measurable indicators within a dimension.

Future studies may emphasize on chemical pollution of water and air (from fuel spills, waste dumping, and exhaust), bio-fouling on hulls and invasive species (from discharge of ballast water) at local and global levels.

**Author’s Contribution**

The single author is involved in Conceptualization, Methodology, Writing- Original draft preparation, Writing-Reviewing and Editing.

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