Calculating the Carbon Footprint in ports by using a standardized tool

Azarkamand, Sahar; Ferré, G.; Darbra, Rosa Mari

doi:10.1016/j.scitotenv.2020.139407

Cited by 27 publications

(28 citation statements)

References 5 publications

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“…Carbon mitigation and carbon sink increment are the two paths most often taken toward achieving urban carbon neutrality. Carbon mitigation is difficult and costly to implement as there is no unified standard for carbon emissions calculation [32], [33], and because many links and stakeholders are involved in this process [34]- [36]. For example, emissions reduction requires cities to transform their roles via cooperation among city-level governments, enterprises, and institutions at substantial costs of both time and financial resources [4].…”

Section: A Carbon Neutralization In Citymentioning

confidence: 99%

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

et al. 2023

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Carbon storage capacity can be estimated to establish evaluation standards and statistics for carbon neutrality. Existing estimation methods including machine learning system have weakness modeling ability, and they are unable to deal with the complex topographies and temporal changes in vegetation in urban zones. However, a deep neural network has the potential ability to face such complex scenes because of its nonlinear fitting properties which has been widely used in industry. In this study, a novel and powerful neural network learning system named CiSL-NPP is proposed, to integrate multi-source data as an accurate, efficient, simple, long-term, city-scale carbon storage capacity measurement method. We use an unsupervised generation model spatio-temporal Masked AutoEncoder (st-MAE) and lightweight RNN model to efficiently obtain high-quality data that contain time-series information such as seasons; this minimizes any undue impact of meteorological, temporal, and spatial factors on the measurement owing to the multi-source, multi-modal nature of the data. In the MAE, we add seasonal coding to make it time-series sensitive; we also embed road network information to accurately perceive the complex topography of the city. Results for 16 cities in China and Europe revealed that the proposed method shows: 1) Higher-quality generated data (MSE is 0.13-0.29); 2) Accurate coverage of time series and complex geographical features; 3) Satisfaction of estimation demands with only 316 RMB cost; 4) Capability to evolve a long-term trend of urban vegetation carbon storage capacity in 4.2 days; and 5) Easily interpretable results which could, in practice, provide sound guidance for urban planning and decision-making processes. INDEX TERMSNeural network Learning System, Unsupervised generation model, Spatio-temporal Masked AutoEncoder, Carbon Storage Capacity Measurement I. INTRODUCTION M ORE than 90% of human economic activity takes place in cities [1], which directly contribute at least 85% of global carbon emissions [2] and are regarded as the world's main battlefields of carbon neutrality [3]. Currently, carbon mitigation and carbon sink increment are the primary approaches to achieving urban carbon neutrality. The carbon mitigation approach, a complex systematic undertaking, is difficult to implement [4]. Further, strict policies towards emissions may have consequences such as carbon leakage [5], [6]. The carbon sink increment approach shows more potential to achieve urban carbon neutrality. For example, studies indicate that the carbon sink of urban vegetation can offset more than 22.45% of carbon emissions [7], already achieving carbon neutrality in some areas [8]. In city, limited to a fixed-size space, the effective way to increase carbon sink is to use green resources (e.g., municipal parks) rationally according to their vegetation carbon storage capacity. Net Primary Productivity (NPP), a crucial metric reflecting the storage effect of vegetation , is the key role for increasing carbon storage capacity in the city ...

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Section: A Carbon Neutralization In Citymentioning

confidence: 99%

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

et al. 2023

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“…Where 𝑇 0 is the beginning time of equipment transition investment; 𝑡 is the phased investment time period; 𝑚 is the initial number of hydrogen-powered ARMGs in the port at 𝑇 0 ; 𝑁 is the total number of ARMGs in the port; and 𝑆 𝑖 is the expected transition process. As shown in Table 1, this research divides the whole process of hydrogen ARMG investment into four stages, including the initial number, 1 4 of the total, 1 2 of the total, and the total number. The probability results of the equipment transition process are given under the general situation through expert interviews.…”

Section: Scene Of the Armgs Low-carbon Transition Processmentioning

confidence: 99%

“…Ports are an important node in the globally integrated transport network. The production and operation activities of ports consume large amounts of energy, which results in serious carbon dioxide emission problems [1]. With the increasingly serious problem of climate change, ports have put forward higher requirements for their emission reduction performance [2,3].…”

Section: Introductionmentioning

confidence: 99%

Towards Optimized ARMGs’ Low-Carbon Transition Investment Decision Based on Real Options

Yang

Meng

et al. 2022

Energies

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As a critical node of the global transportation network, ports have great potential in promoting transportation emission reduction. Promoting the low-carbon transition of ports by using clean energy is effective. Using hydrogen energy in automated container terminals (ACTs) has become popular in port emission-reduction research. The research object is the main port equipment—the automated rail-mounted container gantry crane (ARMG). This research designs a staged investment decision-making scheme for ARMGs’ hydrogen energy transition. The Internet of Things (IoT) architecture in ACTs collects ARMG equipment operation and carbon emission data. It provides a basis for data acquisition in ARMGs’ hydrogen energy transition. Furthermore, ports can adopt big data technology to analyze the correlation between equipment operation and carbon emissions. Finally, the digital twin platform will visualize the ARMG equipment operation and carbon emission behavior to remote operators. These advanced technologies can achieve status monitoring and simulation prediction, which will support ARMGs’ hydrogen energy transition. However, the ARMGs’ hydrogen energy transition has a long cycle, large investment, and strong variability. Ports should make staged investment decisions based on the digital twin platform’s status monitoring and simulation prediction analysis results. Therefore, this research establishes an optimization model for ARMGs’ low-carbon transition investment decision based on the real options method, and analyzes the staged investment scale and timing of ARMGs’ hydrogen energy transition. The results provide a popularized decision-making scheme for the low-carbon transition of ports’ equipment, which could facilitate the low-carbon transition of ports’ equipment.

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“…The primary source of on-site investigation and a follow-up survey in SYDWP collected a large amount of accurate and reliable data, including the daily ship berthing data, yard truck in-out data, daily RTGC operation information, and the daily port throughput, etc. Azarkamand et al (2020) developed a standardized tool to calculate GHG emissions in ports based on WPCI (World Ports Climate Initiative) and IPCC (Intergovernmental Panel on Climate Change) guidelines and the GHG Protocol. This is a fascinating contrast with the following authors who developed a methodology based on international standards and made claims of a thorough bibliographical review but showed no advances or references 12 Comparative analysis of the energy efficiency of container terminals in Colombia according to previous publications of Spengler, Wilmsmeier, ECLAC, or any other authority.…”

Section: Studies Benchmarkmentioning

confidence: 99%

Climate change adaptation and mitigation in ports

Wilmsmeier

2020

Maritime Transport and Regional Sustainability

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Calculating the Carbon Footprint in ports by using a standardized tool

Cited by 27 publications

References 5 publications

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

Towards Optimized ARMGs’ Low-Carbon Transition Investment Decision Based on Real Options

Climate change adaptation and mitigation in ports

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