改革开放四十余年来，中国的经济和社会发展取得了令人瞩目的成就。伴随着经济成长，中国的经济统计也取得了长足进步与发展。1993年，中国国民经济核算体系正式从物质产品平衡表体系（The System of Material Product Balances, MPS）转变为国民账户体系（The System of National Accounts, SNA）。此后，国家统计局先后颁布了《中国国民账户体系（2002）》和《中国国民经济核算体系（2016）》，并开始编制资产负债表和开展全国经济普查。然而，由于统计标准仍然相对落后、统计制度不健全、统计人员素质参差不齐等原因，中国国民经济核算仍然存在一些问题。长久以来，中国的经济统计数据，特别是国内生产总值（GDP），常常受到国际社会、学者和媒体的质疑，在进行经济研究时常受到一定的限制。因此，本文拟采用一种相对独立的测度方法——夜间灯光卫星数据研究中国的经济测度问题。

夜间灯光卫星源于20世纪60年代中期美国国防部的国防气象卫星项目（DMSP），其搭载的实用线性扫描系统（OLS）最初用于收集和监测全球云层覆盖信息。然而，一个意外的发现是，当云层不存在时，该卫星却能够记录人类活动产生的灯光。自上世纪90年代美国国家海洋和气象局（NOAA）将夜间灯光数据电子化以来，与此相关的研究快速涌现，内容涵盖经济增长、人口测度、城市扩张、贫困率、绿色气体排放、灯光污染和灾难管理等。但是，现有文献有关未观测经济、生活水平、经济不平等和发展不平衡等相关的研究还处于初步探索阶段，与中国相关的研究还比较少。因此，本文以上述四个领域为研究主题，并以中国为研究样本探索夜间灯光在经济测度中的应用。

本文共七章，第一章为绪论。第二章为夜间灯光数据概述，包括夜间灯光数据的特征分析、夜间灯光数据的提取与处理、以及夜间灯光数据的研究进展，为全文奠定理论与数据基础。第三章为基于夜间灯光数据的中国地区未观测经济测度研究。未观测经济是中国社会一直存在的普遍现象，但未观测经济的规模及其对经济的贡献却是未知的。本章以夜间灯光卫星数据为基础，通过构建计量模型，并结合世界各国有关未观测经济的调查或测度数据，估算中国1992-2013年中国各省区的未观测经济规模及占比。结果显示，未观测经济占比在各省区非均匀分布，最小值为北京的3.19%，最大值为宁夏的69.71%。福建、浙江、江西、山东、云南、湖北、江苏和青海的NOE占比大约为40%，河南、新疆、甘肃、湖北、黑龙江、贵州和安徽的NOE占比大约为50%-60%，全国平均值为43.11%，东部、中部、东北、西部地区的平均值分别为39.3%, 47.6%, 44.7%和43.6%。

第四章为基于夜间灯光数据的中国地区生活水平测度研究。本章利用城乡住户调查中的人均可支配收入和国民经济核算中的居民消费水平，以全球夜间灯光数据为参照，综合收入端与消费端信息，对1997-2016年中国31个地区的真实生活水平进行比较分析。研究证实，灯光亮度与消费水平和人均可支配收入之间均存在显著的线性关系，可以用来估算真实生活水平。根据不同模型的回归结果得到，真实生活水平的最优无偏估计中消费水平的权重范围为（27.95%，38.31%），人均可支配收入水平的权重范围为（61.69%，72.05%）。相对于消费水平，人均可支配收入更能准确地反映真实生活水平。

第五章为基于夜间灯光数据的中国地区经济不平等测度研究。本章以全球夜间灯光数据为参照，分别从地级和县级行政区层面研究1992-2016年中国地区经济不平等的演化及其影响因素。受地级和县级人均GDP数据可得性和准确性的限制，首先利用省级人均GDP与灯光亮度关系估算地级和县级的经济产出水平。基于地级和县级经济产出水平，采用基尼系数、泰尔指数和阿特金森指数进行不平等研究，证实各省区、全国及四大地区均呈现库兹涅茨倒“U”曲线。对泰尔指数进行四大地区分解，证实四大地区间的经济差异是中国地区不平等的主要根源。

第六章为基于夜间灯光数据的中国区域平衡发展测度研究。本章以夜间灯光数据为基础，通过构建人口-灯光基尼系数、空间-灯光基尼系数和经济-灯光基尼系数，从人口、空间、经济三个维度研究1992-2016年中国区域平衡发展问题。理论分析表明，三维灯光基尼系数分别测度经济发展成果、经济发展能力和经济发展质量的地区不平衡。实证分析显示，三维灯光-基尼系数的演化呈现不同的趋势，人口-灯光基尼系数呈下降趋势，空间-灯光基尼系数和经济-灯光基尼系数分别呈现倒U状和U状。进一步分析发现，政策实施、人口流动、技术进步等是导致这种现象的主要原因。区域平衡发展的影响因素分析表明，几乎所有因素对经济发展成果、经济发展能力和经济发展质量的影响效应均是不相同的，因此很难制定某个经济政策能够同时降低三者的地区不平衡。在制定平衡发展的政策时，要正确厘清发展能力、发展机会和发展结果三者之间关系，根据具体形势区分三者的优先等级，逐步实现地区平衡发展。

最后一章为本文主要结论与政策含义。本文的研究结论有助于更加准确、全面地认识中国经济发展和生活水平的真实状况，以及经济不平等和区域平衡发展的演化，能够为政府和管理部门制定更加有效、合理的经济政策提供重要参考。

Over the past 40 years following the implemention of the reform and opening-up policy, the economy and society of China have made remarkable achievements. Along with it, China's economic statistics have also made considerable progress and development. In 1993, China's national accounting system was formally transformed from the System of Material Product Balances (MPS) to the System of National Accounts (SNA). Since then, the National Bureau of Statistics has successively published "China National Accountting System (2002)" and "China National Accountting System (2016)", and began to prepare the balance sheets and implement the national economic census. However, due to the relatively backward statistical standards, the imperfect statistical system, and the uneven quality of statisticians, there are still some problems in China's national accounting system. For a long time, China’s economic statistics, especially the gross domestic product (GDP), have often been questioned by the international community, scholars and the mass media, and been subject to certain restrictions when conducting economic research. Therefore, this paper intends to use a relatively independent measurement method—the nighttime lights data to study China's economic measurement problems.

The nighttime lights satellite originated from the Defense Meteorological Satellite Program (DMSP) of the United States Department of Defense in the mid-1960s, and the Operational Linescan System (OLS) was originally designed to collect and monitor the global cloud cover information. An unexpected finding, however, was that the satellite was able to record human-generated lights when the clouds did not exist. Since the National Oceanic and Meteorological Administration (NOAA) digitized nighttime lights data in the 1990s, related literature has emerged rapidly, which covers economic growth, population, urban expansion, poverty, green gas emissions, light pollution and disaster management. However, the research on the non-observed economy, living standards, economic inequality, and development imbalance is still in the preliminary exploration stage, and there are relatively few studies related to China. Therefore, this paper is mainly concerned with the above four fields, and takes China as a research sample to explore the application of nighttime lights in economic measurement.

This paper consists of eight chapters. The first chapter is the introduction. The second chapter is an overview of nighttime lights data, including the feature of nighttime lights data, the extraction and processing of nighttime lights data, and the literature review on the nighttime lights data, which lays the theoretical and data foundation for the paper. 

The third chapter is measuring the regional non-observed economy in China with nighttime lights. The NOE has always been a pervasive phenomenon in China’s provincial regions, but the size of the NOE and its contribution to the overall economy remain mystery. This chapter intends to measure the share of the NOE in China using the nighttime satellite imagery data combined with countries or regions throughout the world. The results show that the NOE share averaged between 1992 and 2013 for China’s provincial regions is unevenly distributed, with the smallest value being 3.19% for Beijing and the largest value being 69.71% for Ningxia. Additionally, Fujian, Zhejiang, Jiangxi, Shandong, Yunnan, Hubei, Jiangsu and Qinghai have similar figures of approximately 40%, while the figures for Henan, Xinjiang, Gansu, Hebei, Heilongjiang, Guizhou and Anhui are between 50% and 60%. The national average is 43.11% while the figure for the eastern region, middle region, north-eastern region and western region are 39.3%, 47.6%, 44.7% and 43.6%, respectively.

The fourth chapter is comparison of regional living standards across China based on the nighttime lights data. This chapter synthesizes the information from the income and consumption end to analyze the real living standards in the 31 regions across China between 1997 and 2016 using Household Final Consumption Expenditure (HFCE) per capita in the national accounting system and Disposable Income per capita in the household survey with the global nighttime lights data as reference. The results show that there exists significant linear relationship between lights intensity and HFCE per capita or Disposable Income per capita, which gives the evidence that night-time lights can be used to estimate the real living standards. According to the regression results of different models, the optimal weight for HFCE per capita in the best linear unbiased estimator of the real living standards is between 27.95% and 38.31%, while this figure for Disposable Income per capita is from 61.69% to 72.05%. Compared with HFCE per capita, Disposable Income per capita can measure the real living standards more accurately.

The fifth chapter is measuring regional inequality in China based on the nighttime lights data. This chapter studies the regional inequality in China and its influence factors from 1992 to 2016 in the administrative level of prefecture and county based on the nighttime lights data. Subject to the availability and accuracy of GDP per capita in prefecture and county level, this paper first measures the economic output in the above two levels based on the relationship between lights and provincial GDP per capita. Then, Gini coefficient, Theil index and Atkinson index are calculated to measure regional inequality using the estimated economic output, which suggests Kuznets inverted U curve exists both in the nation and in the provincial regions. Decomposition on Theil index shows the economic disparity between the four big regions is the main driving force for region inequality.

Chapter 6 is measuring regional development imbalance in China based on the nighttime lights data. This chapter constructs Demograhpic Lights Gini coefficients, Spatial Lights Gini coefficients and Economic Lights Gini coefficients based on nighttime lighs to study the regional development imbalance in China between 1992 and 2016 from the perspective of population, space and economy. Theoretical analysis shows that the three-dimensional lights Gini coefficients measure regional imbalances in economic development achievement, economic development capacity, and economic development quality, respectively. The empirical analysis shows that the evolution of the three-dimensional lights Gini coefficients has different trends with Demograhpic Lights Gini coefficients a downward trend, Spatial Lights Gini coefficients an inverted U-shape and Economic Lights Gini coefficients a U-shape. A further analysis shows that policy implementation, population migration, and technological progress are the main reasons for this phenomenon. The analysis of influencing factors of regional development imbalance shows that almost all the factors have different effects on economic development results, economic development capacity and economic development quality, so it is difficult to formulate an economic policy that can reduce the regional disparities of the three at the same time. When formulating regional development policy, the relationship between the development capability, development opportunity and development achievement should be correctly clarified. Priorities should be made according to the specific situation, and balanced regional development should be achieved gradually.

The last chapter conludes. The conclusions of this paper can help us understand the true state of regional economic development and regional living standards and the evolution of regional inequality and regional development imbalance in China in a more accurate and comprehensive way, which can be a reasonal reference for the government and management department to formulate more effective, and reasonable economic policies.