跳到正文
← 返回论文列表与筛选
阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要依据生产技术和成本最小化条件估计技术参数与无效率,直接对应结构估计。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者

好产出、坏产出与技术:环境生产模型中的内生性

研究问题
当企业生产期望产出的同时产生污染副产品时,如何分别估计技术与环境效率,并处理投入和坏产出的内生性?
方法与思路
作者组合好产出与副产品技术,利用成本最小化一阶条件处理投入内生性,并以贝叶斯 MCMC 估计技术及无效率。
材料陈述的贡献
摘要提出同时估计两类技术和无效率的框架,称部分模型自动处理坏产出内生性,并比较有无内生性校正的模型。
阅读关注点
应核对需求决定电力产出而视其外生的依据、一阶条件如何形成估计方程,以及两类无效率和内生性的区分。
阅读边界
摘要未提供分布假设、识别条件、数据规模或比较数值;不能由采用内生性校正直接认定效率估计更准确。

原文摘要

In this paper we consider an environmental production process in which firms intend to produce outputs (which we label as desirable/good) but the production process is such that it automatically produces some other unintentional but inevitable undesirable (bad) outputs as by-products (emission of pollutants). Like stochastic production frontier, by-production technology specifies that there is a minimal amount of the by-product that is produced, given the quantities of inputs and desirable outputs. The presence of (environmental) inefficiency in by-production therefore means that more than this minimal amount of the undesirable output is produced. Similarly, the presence of technical inefficiency implies that, given inputs, less than the maximal possible amount of desirable outputs is produced. Alternatively, it means that more than the minimal amounts of inputs are used to produce a given level of desirable output. We use the “by-production technology” approach which is a composition of production technology of desirable outputs and the technology of by-products, and estimate both technical and environmental efficiency. Given that electricity, the good output in our application, is demand determined, we treat it as exogenous and address the endogeneity of inputs by using the first-order conditions of cost minimization. Some of our models automatically take endogeneity of bad outputs into account. We use an efficient Bayesian MCMC technique to estimate both good and bad output technologies and both types of inefficiency. We also compare results with some alternative models with and without endogeneity corrections.

笔记与分组