即时配送经济对城市居民生活碳足迹的替代与溢出效应研究

为破解电商平台在绿色营销中用户识别不准确的难题，提出利用居民线下绿色出行行为预测其线上绿色服务偏好的新思路。基于全国85个城市的调研数据，采用混合Logit模型量化分析居民出行决策。研究发现：1) 居民绿色出行意愿存在显著的群体差异，男性、企事业员工及学生是核心积极群体，而家庭拥车数量为主要阻碍；2) 揭示用户为换取公共交通的长期效率而“理性妥协”接受接驳时间的行为模式。基于此，构建了“绿色通勤者”、“无车生活家”等可直接应用的电商用户画像，并为平台提出三大精准营销策略：实施基于价格敏感度的梯度激励、以确定性承诺为核心的“绿色准时达”服务以及开展面向特定画像的场景化服务。为电子商务平台提升绿色营销效率、构建可持续竞争优势提供了实证依据与落地路径。

在“双碳”目标与数字经济深度融合的背景下，电商平台如何通过绿色营销策略激活消费者的社会责任认知成为关键议题。本研究基于SOR理论框架，构建“绿色策略–心理机制–认知激活”模型，探讨电商平台绿色营销策略对消费者社会责任认知的影响路径。通过问卷调查与结构方程模型分析，研究发现：(1) 绿色营销策略的激活效果呈现显著差异，行为激励机制与绿色产品认证的效应最强；(2) 环保效能感与情感共鸣构成双路径中介机制，表明消费者通过理性计算与情感认同协同构建责任认知；(3) 个体环保价值观与平台声誉形成双重调节边界，高价值观群体对认证策略更敏感，而平台声誉通过信任背书放大行为激励的效果。研究提出“精准适配 + 信任重构”的实践路径，建议平台设计分层策略并借助区块链等技术提升绿色供应链透明度，为平台经济的可持续发展提供了理论与操作指引。

随着电子商务规模持续高速增长，我国城市道路交通结构和运行特征发生显著变化，电商物流、即时配送及城市货运需求快速攀升，成为城市交通碳排放的重要推动力量。在“双碳”战略背景下，厘清电商驱动的交通碳排放变化特征，对构建绿色电商供应链和推进城市低碳转型具有重要意义。本文以成都市为例，基于政府统计数据、交通部门资料及相关文献，构建自下而上的道路交通碳排放测算模型，对2018~2022年城市配送相关交通工具的碳排放进行量化评估。研究发现：(1) 受电商购物增长和快递物流需求攀升影响，载货汽车与出租车碳排放显著增加，其中载货汽车排放五年增长约56%，出租车增长超过200%；(2) 私人汽车及摩托车因同城配送、即时配送等业务量扩张而呈现隐性增长趋势；(3) 公共交通低碳化成效明显，但与电商物流关联度较低。基于此，提出推动配送车辆新能源化、建设前置仓及共同配送体系、利用平台算法优化路径、加强网约车配送监管等策略，以期为城市在电商时代的绿色交通治理提供参考。

即时配送“最后一公里”面临效率瓶颈、成本攀升与体验难平衡的挑战。无人机与骑手协同配送被视为破局关键，但其系统性价值有待量化验证。本研究构建了一个整合运营效率、经济成本与用户体验的多目标动态调度模型，并设计了自适应大邻域搜索(ALNS)算法进行求解。通过仿真实验，对比分析了纯骑手模式、静态协同规则与人机协同优化模式的性能。数值实验表明，人机协同优化模式能实现显著的协同效应：与纯骑手模式相比，总成本降低约12.9%，订单准时率提升至94.7%，平均配送时长缩短17.4%。协同效益受无人机单位成本、订单密度及区域特征显著影响，存在关键成本阈值。本研究首次将“用户体验”以非线性延误惩罚函数形式内生于协同调度模型，为平台提供了可量化权衡“效率–成本–体验”的战略决策工具，并提出了优先在高潜力区域部署的精准投资建议。

随着游戏化应用的日益发展，游戏化元素的设计如何影响消费者低碳行为的研究愈加受到营销学者的关注。绿色环保App使用率的提升，成为推动线上环保活动和绿色生活方式的有力工具。已有研究表明游戏化元素的加入对居民低碳行为有助推作用，但并没有深入理解游戏化设计如何激发游戏化示能性并进而影响低碳行为的内在机制。为了填补这一空白，该研究以国内最具影响力的在线环保应用“蚂蚁森林”为例，通过扩展游戏化示能性理论和目标框架理论，探索消费者低碳行为的驱动因素。结果表明，游戏示能性是蚂蚁森林用户践行低碳行为的重要心理来源。本研究为电商平台如何进行游戏化设计更好激发游戏化示能性对低碳行为的驱动作用提供参考。

随着城市末端物流配送需求规模的持续扩张与服务时效要求的不断提高，传统以路面车辆为主的配送体系正面临运营成本攀升、交通拥堵加剧及环境负外部性显著等系统性挑战。在此背景下，利用城市公共交通系统剩余运力进行货运(Freight on Transit, FoT)作为一种集约、可持续的协同配送模式受到广泛关注。然而，现有研究在运输组织模式的界定与定量分析框架方面仍较为分散，缺乏系统性整合与模式间的比较分析。为此，本文对FoT领域的定量研究文献进行系统性梳理与整合。首先，界定了FoT系统的三阶段流程与四类核心运营要素，并据此归纳出五类典型运输组织模式，系统比较各模式的流程结构、协同机制、优势、局限及适用场景。其次，依据战略、战术与操作层级，梳理并归类出现有研究聚焦的选址分配、列车调度与路径规划三类核心优化问题，综述各类问题的数学建模方法、求解算法及其在不同模式中的研究侧重点。最后，总结现有研究的共识与不足，并展望未来研究方向，包括多模式统一建模、多层级决策耦合建模，以及不确定环境下动态调度机制的引入。研究结果为城市公共交通与末端物流系统的协同设计与优化提供了理论框架，也为高效、低碳的城市货运方案设计提供了参考。

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This study estimates the combined direct and indirect rebound effects from energy efficiency improvements in the delivery of six energy services to UK households, namely: heating; lighting; cooking; refrigeration and clothes washing; entertainment and computing; and private vehicle travel. We use a unique database on the price and quantity demanded of these energy services over the past half century. We estimate a two-stage almost ideal demand system for household expenditure, using these energy services as expenditure categories. We estimate rebound effects in terms of carbon emissions and only include the ‘direct’ emissions associated with energy consumption. Our results suggest direct rebound effects of 70% for heating, 54% for private vehicle travel and ∼90% for the other energy services. However, these effects are offset by negative indirect rebound effects—that is, indirect rebounds contribute additional emission savings. As a result, our estimates of combined rebound effects are generally smaller, namely 54% for lighting, 55% for heating, 41% for refrigeration and clothes washing, -12% for entertainment and computing, 44% for cooking and 69% for vehicle travel. We also find some evidence that rebound effects have declined over time. We provide some important caveats to these results, and indicate priorities for future research.

One of the fundamental concerns in the analysis of logistical systems is the trade-off between localized, independent provision of goods and services versus provision along a centralized infrastructure such as a backbone network. One phenomenon in which this trade-off has recently been made manifest is the transition of businesses from traditional brick-and-mortar stores to retail sales facilitated via e-commerce, such as grocery delivery services. Conventional wisdom would dictate that such services ought to be more efficient—say from the perspective of the overall carbon footprint—because of the economy of scale achieved by aggregating demand through a delivery van, as opposed to the many separate trips that customers would otherwise take using their own means of transport. In this paper, we quantify the changes in overall efficiency due to such services by looking at “household-level” economies of scale in transportation: a person might perform many errands in a day (such as going to the bank, grocery store, and post office), and that person has many choices of locations at which to perform these tasks (e.g., a typical metropolitan region has many banks, grocery stores, and post offices). Thus, the total driving distance (and therefore the overall carbon footprint) that that person traverses is the solution to a generalized travelling salesman problem (GTSP) in which they select both the best locations to visit and the sequence in which to visit them. We perform a probabilistic analysis of the GTSP under the assumption that all relevant locations are independently and identically distributed uniformly in a region and then determine the amount of adoption of such services that is necessary, under our model, in order for the overall carbon footprint of the region to decrease.

The comfortable perception that global environmental challenges can be met through marginal lifestyle changes no longer bears scrutiny. The cumulative impact of large numbers of individuals making marginal improvements in their environmental impact will be a marginal collective improvement in environmental impact. Yet, we live at a time when we need urgent and ambitious changes. An appeal to environmental imperatives is more likely to lead to spillover into other pro-environmental behaviours than an appeal to financial self-interest or social status.

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This paper examines the carbon impact of online retailing and compares it with that of conventional retailing. It discusses the effect of varying the scope of the calculation, the system boundaries and the underlying assumptions. While most of the carbon emissions come from the last-mile delivery, this is also the activity whose carbon intensity is most sensitive to assumptions made about consumer behavior. On the basis of an extensive literature review, the paper also explores the carbon impacts of the upstream supply chain, energy use in information and communication technology and several aspects of travel behavior. This should help researchers to make wider and more realistic assessments of the environmental impact of online retailing. On the basis of these assessments, one can test the conditions under which online shopping is likely to have a lower carbon footprint.