Productivity and Labor Density: Agglomeration Effects over Time

Can Innovation Agglomeration Reduce Carbon Emissions? Evidence from China

“…Different from the traditional neoclassical model, the output density model proposed by Ciccone and Hall (1996) allows for increasing returns to scale, and this model fully considers the influence of spatial factors on output, thus providing a basic analytical framework for depicting spatial production activities [ 43 ]. Ushifusa and Tomohara (2013) [ 39 ] further simplified the output density model as follows: where Q i and A i are respectively non-farm output and total area of the region; q i is the unit area non-agricultural output of the i region, namely the output density; τ i is the Hicks neutral parameter; n i is employment density; (0 < < 1) represents the income share of labor to capital input; and k i is the capital input per unit area. The main difference between Equation (1) and traditional Cobb–Douglas production function is that it contains the parameter (0 ) representing the income share of the two inputs, capital and labor, to land.…”

“…It reflects the diminishing marginal productivity of the factors caused by crowding. The smaller represents the lower factor production efficiency [ 39 ]. Ciccone and Hall (1996) [ 43 ] identified the loss of productivity due to additional factor input per unit area as congestion effect, and give the following explanation: Under the condition of Hicks neutral technology, land elements are relatively fixed.…”

“…This paper is consistent with the theoretical model. Referring to the measurement method of output density model [ 39 ], this measurement of innovation agglomeration can describe the spatial density and distribution of innovation activities. Patent data are often used because of their palpable link with innovation output and their relative integrity [ 68 ].…”

“…The spatial dynamic econometric model is used to test the relationship among innovation agglomeration, energy intensity, and carbon emission, which can control both spatial and endogenous effect. The marginal contribution of this research is threefold: (1) The output density model proposed by Ushifusa and Tomohara (2013) was extended in this paper [ 39 ]. We put energy and carbon emission into the output density model, and mathematically explain the effect and mechanism of innovation agglomeration and energy intensity on carbon emission under different threshold conditions.…”

Impact of economic agglomeration on regional water pollutant emissions and its spillover effects

“…自 然 资 源 学 报 经济增长是各地共同的产业政策取向，以期通过经济活动的空间集聚获得各种溢出和规 模经济等收益。针对经济集聚与污染排放关系这一命题，既有研究主要从二者影响机制 的理论分析、以及影响程度的实证研究展开。前者通过理论推导、加入环境变量的均衡 模型演绎等方式，刻画了作用关系及其外部性特征 [14][15] 。实证研究则存在两种观点：一方 面，认为经济集聚伴随产业规模扩张，污染物排放就可能增加。流域、省域层面面板数据 显示经济集聚会导致环境恶化 [16][17][18] ，特别是工业经济的集聚过程加剧水体和空气污染 [19][20] ， 微观层面的分析也证实了制造业集聚和水污染的关联性 [21][22] 。另一方面，也有学者发现经 济集聚的减排效应，认为经济集聚能够促进技术进步与技术扩散，为环保型生产技术、 污染处理设施集中提供可能 [23][24][25] ，集聚还有利于劳动生产率及经济发展水平提升，推动实 施更严格的环境规制降低污染 [26][27][28][29] 画经济集聚的指标未考虑微观尺度下地理单元差异所产生的空间偏倚。鉴于此，Ciccone 等 [33] 、刘习平等 [34] 、张翠菊等 [35] 发现，相对于上述指标，单位面积城市土地上的 GDP 产 出更能衡量城市经济活动的集聚程度，同时，GDP 中包含了农业产值，但农业产值更多地 是在耕地、园地等农业空间实现。因此，借鉴生产密度模型 [36] [40] ，可用下式表示： Abstract: Based on the database of COD and NH3-N emissions from 339 cities at prefecture level in China, this paper explores the impact of economic agglomeration on regional water pollutant emissions and the differences in the impact levels between different city-size by using the econometric model. Meanwhile, a bivariate spatial autocorrelation method is adopted to analyze the spillover effect of economic agglomeration on water pollutant emissions, which provides a reference for making a benign interactive countermeasure to promote economic agglomeration and pollution mitigation.…”