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The widespread use of digital technology has driven a huge change in the way payments are made. Based on the micro-data of the China Family Panel Studies (CFPS), this paper explores the relationship between mobile payment and carbon emissions from the consumption side. With the exogenous impact of Alipay's "Collecting Five Blessings" campaign, this paper finds that mobile payment significantly increases household consumption carbon emissions, but the marginal effect is gradually decreasing. Mechanism analysis shows that mobile payment mainly realizes the above impact through promoting the increase of consumption expenditure and upgrading of consumption structure. Further analysis indicates that carbon reduction policies introduced by the Chinese government in the personal consumption sector can mitigate the carbon emissions growth associated with mobile payments. Still, there is a need to strengthen policy measures further to effectively address the issue of rising carbon emissions from household consumption. [ABSTRACT FROM AUTHOR]

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain. [Display omitted] • Energy consumption is divided into six parts based on the intermediate destination. • Fifteen factors is decomposed based on index decomposition analysis. • The contributions of the structure, proportion, and efficiency changes is considered. • This paper shows a way of identifying the composition of energy consumption. [ABSTRACT FROM AUTHOR]

As a critical infrastructure to support the development of digital economy, the scale of data center (DC) power consumption has been growing continuously in recent years. The plug-in of a large number of DCs to the distribution network (DN) presents both opportunities and challenges for the secure, stable, and sustainable operation of the system. In order to address the planning issues arising from the integration of DCs into the DN, this paper proposes a two-stage (planning stage and operation stage) collaborative planning method for DCs and the DN while considering the source-demand uncertainty. Firstly, spatial-temporal demand response characteristic of DCs is explicitly elaborated, and based on the energy consumption model, a two-stage stochastic planning model considering conditional value at risk (CVaR) for DCs and the DN is constructed using probabilistic stochastic simulation, which obeys low-carbon and security, and satisfies the source-network-load constraints of the system. The siting and sizing for DCs and renewable generation, feeder expansion for the DN are determined to minimize the investment cost during the planning stage, while the power output for the devices considering uncertainties from load and renewable energy are optimized to minimize the operation cost during the operation stage. Secondly, the L-shaped decomposition algorithm is proposed to solve the two-stage stochastic optimization problem by reconstructing the problem into a master problem and subproblems, where the dual multiplies are calculated to generate feasibility cuts and optimality cuts to achieve iterative solving. Finally, numerical case studies on a 33-bus distribution system demonstrate that the participation of DCs in demand response can achieve a significant reduction in the investment and operation cost of the DN, and further promote the consumption of distributed renewable energy sources. • The spatial-temporal characteristics of DC demand response is explicitly elaborated and the mathematical formulation is proposed. • A two-stage stochastic collaborative planning approach of DC and DN based on CVaR is proposed. • Guidance for investment decisions for DSO with large numbers of DCs plug-in under uncertainties. • Demand response of DCs can effectively support renewable energy consumption and reduce the cost of DN. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

Distributed renewable energy has developed rapidly in recent years, but its low-carbon value is usually ignored due to its limited individual capacity. It will be an important business for electricity retailers to aggregate these distributed renewable resources and make use of their carbon emission reduction benefits. Therefore, this paper proposes a business model for electricity retailers based on electricity-carbon coupling retail packages. Firstly, a framework of the business model is proposed to support distributed renewable energy transaction among the electricity retailer, prosumers, and consumers. Secondly, dynamic carbon evaluation mechanism-based electricity-carbon coupling retail packages are designed, which contain three types of generation packages for prosumers (e.g., fixed subsidy package, dynamic subsidy package and daytime generation package) and three types of consumption packages for consumers (e.g., daytime consumption package, nighttime consumption package and minimum consumption package). In addition, a single-leader and multi-followers Stackelberg game model is constructed to optimize the designed packages. Especially, the electricity retailer aims at maximizing its profit considering conditional value at risk (CVaR) as the leader. Meanwhile, prosumers and consumers respectively aim at maximizing their profit satisfaction and comprehensive satisfaction as followers. Finally, the particle swarm optimization (PSO) algorithm and CPLEX commercial solver are both used to obtain the model's Nash equilibrium (NE). Example analysis verifies the designed electricity-carbon coupling retail packages can utilize the low-carbon value of distributed renewable resources and improve the benefits and satisfaction of prosumers and consumers. • The DCE mechanism is introduced to evaluate the low-carbon value of renewable energy. • Three types of generation packages and three types of consumption packages are designed. • A bi-level SLMF game model is established to describe the trading of the ER and its users. • The impacts of the packages on participants' profit and utilization of the DRE are analyzed. [ABSTRACT FROM AUTHOR]

Battery electric industrial vehicles (BEIVs) offer significant environmental protection and decarbonization potential for rapidly growing logistics and transportation sectors in China. However, the limited driving range remains a major obstacle to the widespread adoption of BEIVs. Eco-driving is an effective solution that can improve energy efficiency and extend the driving range of vehicles by controlling driving behavior and speed. Therefore, this paper introduces an energy efficient speed planning method for BEIVs, in which data-driven models are developed to estimate energy consumption. Real-world driving data collected from a BEIV are used to model the energy consumption by curve fitting, random forest, and LSTM. The speed planning process is considered a Markov Decision Process, which is solved using Dynamic Programming and Q-learning, respectively. The case study of a battery electric forklift shows that speed planning can reduce energy consumption by 13%–27% compared to time-optimal speed, while only increasing the driving time by 10%–20%. • Energy efficient speed planning methods for electric industrial vehicles are proposed. • The speed planning method uses data-driven vehicle energy consumption models. • The energy consumption models are developed using random forest and LSTM. • The speed planning problem is formulated as a Markov decision process. • The speed planning problem is solved by Dynamic Programming and Q-learning. [ABSTRACT FROM AUTHOR]

Modern agriculture involves the cooperation of stakeholders in multi-fields where their interaction significantly impacts the planning and operation of rural power systems. However, the traditional planning and operation for rural power systems assumes the power consumption as a given input without considering its unique feature caused by human factors. Particularly in China's rural areas, power consumption will rapidly increase and decrease due to the human-factor-based activity of farmer households' off-farm employment in urban and their home-returning during some important festivals. Therefore, this work proposes a multi-agent decision-making framework to realize the planning and operational optimization of power systems in a human-factor-based rural community. The framework integrates multiple agents' decision-making models including various stakeholders in modern agricultural systems and analyses their interaction. The meteorology-load and meteorology-generation models of power are materialized and embedded in the intensive agriculture and power system agent's decision-making models, which are used by the two agents to estimate the power consumption and photovoltaic (PV) generation, respectively. This work first quantizes the macro choice process of human-factor-based farm and off-farm employment and the numbers innovatively. Further, the farmer behavior for farm employment is simulated by the agent-based modeling system, and the power consumption of the farmer household group is estimated. Based on the overall agents' power consumption in the community, the planning and operation model of the power system is proposed to decide the optimal size and dispatch of the electrical devices. A multi-agent rural community is conducted for the case study to verify the effectiveness of the proposed decision-making framework. The results show that the farmer households who choose off-farm employment account for 66 % of the total households and their power consumption in most of the daytime on weekends is higher than on weekdays. The transportation system agent decides to purchase the electric truck with 5 tons of struck capacity for crop transportation. The power system agent decides to construct a hybrid system of PV, battery energy storage, and a transmission line of less transmission capacity, which can reduce its total system cost. Additionally, this paper finds that the solar radiation obtained by the greenhouse, which is considered the intensive agriculture agent, and its power consumption show opposite variation tendency, and the power consumption of the farmer household group in the Spring Festival is the largest. • A multi-agent decision-making framework of a rural community is proposed. • A meteorology-load and meteorology-generation models are materialized. • A macro choice process of human-factor-based farm/off-farm employment is quantized. • A bottom-up estimation method of power consumption is established by farmer behavior. [ABSTRACT FROM AUTHOR]

This paper investigates the causal effect of COVID-19 quarantine measures on the electricity consumption of different types of regulated customers. In particular, the average energy consumption of a group of customers in Chile is examined, exploiting the weekly variations in the application of the stages of the Step by Step Plan as a natural experiment. Step by Step Plan was the Chile COVID containment strategy, which involved implementing partial lockdowns based on the COVID incidence in the municipalities, and adjusted on a weekly basis. An econometric model is specified, and estimated using the technique Difference-in-Difference-in-Difference (DDD) to quantify the causal impact of these measures. After applying the model, it is found that the quarantine measures have had a negative effect on the trend weekly consumption of customers located in municipalities where some of the phases of the Plan have been applied. This effect is significantly different depending on each stage of the plan analysis, and statistically significant. It is also observed that, on average, the savings for a representative customer in the sample range from $ 8 to $ 6. 15 for some municipalities, and the sample costs range from $ 0. 02 to $ 0. 26 for others, depending on the municipality analyzed. The results can be expressed in a total of 2.95 tons less CO 2 emitted on a monthly basis. This shows a great impact in terms of sustainability and planning of energy demand and supply. • Quarantines by Covid 19 affected energy consumption, like a "Natural Experiment". • Using Panel Data of 400 thousand observations with geographic, temporal and Daylight Saving Time controls. • Difference-in-Difference-in-Difference method to quantify causal impact of quarantines on electric consumption. • Applying quarantines generated Energetic Savings from 8 cents to 6.15 dollars depending on client type. • An Event Study was designed to check the estimation results. [ABSTRACT FROM AUTHOR]