The difficulty of this question is relatively low for the investigation of the model, and the difficulty lies in the collection, selection and processing of data.

Here is the recommended website for data query: China Carbon Accounting Database (CEADs)

https://www.ceads.net.cn/

country data

National data data.stats.gov.cn/easyquery.htm?cn=C01

And "Statistical Yearbook" of various provinces and cities, "Statistical Yearbook of China Construction Industry", "Annual Jiangsu Construction Industry Development Report", etc.

Question 1: Given the building data, assuming that the temperature in the building needs to be kept at 18-26 degrees all the time, when the temperature is not suitable, the temperature must be adjusted by electricity, and the consumption of one degree of electricity is equivalent to 0.28 kilograms of carbon emissions . Please calculate the annual carbon emissions of the building that adjusts the temperature through air conditioning (assuming that the air-conditioning heating performance coefficient COP is 3.5, and the cooling performance coefficient EER is 2.7). (Try to use the conditions given in this question to calculate carbon emissions, without considering other losses)

Idea summary : First, you need to find relevant information to understand the concept definition of COP and the formula for calculating carbon emissions.

Heating coefficient of performance COP: COP is a measure of the performance of the compressor in heating, and its unit is (W/W). Air conditioner COP refers to the heating coefficient of performance of the air conditioner, which is mainly the ratio of the heating capacity generated in the heating cycle to the power consumption consumed by heating. The higher the COP value, the higher the heating capacity generated under the same power consumption, and the better the heating performance of the air conditioner.

Calculation method: COP=Qh/W, Qh refers to the nominal heating capacity per unit time, unit W, W refers to the power consumed by the air conditioner per unit time, unit W. COP=heating capacity of air conditioner/heating power of air conditioner

Refrigeration performance coefficient EEP: Definition: EER is a measure of the performance of the compressor in refrigeration, and its unit is (W/W). Air conditioner EER refers to the cooling performance coefficient of the air conditioner, which is mainly the ratio of the cooling capacity generated in the refrigeration cycle to the electrical power consumed by cooling. The higher the EER value, the higher the cooling capacity generated under the same power consumption, and the better the cooling performance of the air conditioner.

Calculation method: EER=QC/W, where QC refers to the nominal cooling capacity per unit time, unit W, W represents the power consumed by the air conditioner per unit time, unit W. EER = cooling capacity of air conditioner / heating power of air conditioner.

Thermal conductivity refers to the heat transfer through an area of ​​1 square meter within one hour for a material with a thickness of 1m and a temperature difference of 1 degree (K, ℃) between the two sides of the material under stable heat transfer conditions. The unit is W/m. Degree (W/(m·K), where K can be replaced by ℃). Heat conduction equation : Q=kA(T1-T2)/L Among them, Q represents the heat transfer, k represents the thermal conductivity, A represents the heat transfer area, T1 and T2 represent the temperature at both ends, and L represents the thickness

Then we can deduce the electricity consumed by the building according to the formulas of COP and EER and known data, and then calculate the carbon emissions.

Steps to solve the problem : From the average temperature data of 12 months given in the title, first find out the months that need heating/cooling, and those beyond the range of 18-26 degrees involve a total of 9 months. Taking January as an example, when -1 degree is less than 18 degrees, heating is required, and the heating heat is calculated as follows:

And so on to get the calculation code of each month:

import numpy as np
import pandas as pd
temperature=[-1,2,6,12,22,28,31,32,26,23,15,2]
days=[31,28,31,30,31,30,31,31,30,31,30,31]
def HeatingCarbonEmissions(t):#制热
    dt=18-t
    q=dt*(0.3*37/0.3+1.6*5/0.3+0.2*12/0.3+0.25*12)
    w=q/3.5*0.001
    c_e=w*0.28
    return c_e
def CoolingCarbonEmissions(t):#制冷
    dt=t-26
    q=dt*(0.3*37/0.3+1.6*5/0.3+0.2*12/0.3+0.25*12)
    w=q/2.7*0.001
    c_e=w*0.28
    return c_e
carbonemissions=[]
for i in range(12):
    t=temperature[i]
    d=days[i]
    value=0
    if t<18:
        value=HeatingCarbonEmissions(t)
    elif t>26:
        value=CoolingCarbonEmissions(t)
    value=value*d
    carbonemissions.append(value)
print(carbonemissions)
print('总和：',sum(carbonemissions))
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
plt.bar(range(1,len(carbonemissions)+1),carbonemissions,fc='g')
# 设置图片名称
plt.title("carbon emission")
# 设置x轴标签名
plt.xlabel("month")
# 设置y轴标签名
plt.ylabel("kg")
plt.show() 

result

2. Question 2: In the entire life cycle of residential buildings (construction, operation, demolition), there are many factors that affect carbon emissions, such as architectural design standards, climate, production and transportation of building materials, regional differences, energy consumption for construction and demolition, and decoration style , energy consumption, building type, etc. Please search and analyze data, establish a mathematical model, and find indicators that are highly correlated with the above factors and are easy to quantify, and based on these indicators, conduct a comprehensive evaluation of the carbon emissions of the entire life cycle of residential buildings.

Summary of ideas : This question is relatively open, and the focus is on the selection of indicators and data collection. The selected indicators must be supported by highly relevant data and easy to quantify. After selecting the indicators, the traditional comprehensive evaluation model can be used to calculate the weight score of the residential building influencing factors. (topsis entropy weight method, rank sum ratio comprehensive evaluation method, analytic hierarchy process, etc.), here you can also use the random forest method to score each factor based on carbon emissions.

Some steps show:

random forest method

Question 3: On the basis of Question 2, consider the carbon emissions of the three stages of the building life cycle, search for relevant information, establish a mathematical model, and conduct a comprehensive evaluation of the carbon emissions of residential buildings in 13 prefecture-level cities in Jiangsu Province in 2021. And the validity of the built evaluation model is verified.

Idea summary : According to the results of the second question, we get the emission weights of the three stages.

It is only necessary to collect the corresponding indicators of prefecture-level cities in Jiangsu Province, and then linearly weight them to obtain the score.

Validity can be verified by adding noise to the index data or analyzing and evaluating the sorting results in text form in combination with actual data.

The challenge remains in data collection.

Solution : Since the data of specific cities in Jiangsu Province is difficult to obtain directly, the data of Nanjing, Shanghai, Hangzhou and other provincial capitals are easier to obtain. Considering that Jiangsu Province and Shanghai are closely related in terms of economic and industrial development as well as geographical location, we rely on available GDP, climate and other data to make analog estimates.

Question 4: Accurate carbon emission forecasting can provide an important reference for formulating emission reduction policies and optimizing low-carbon building design. Establish a carbon emission prediction model, based on the historical data of carbon emissions in the whole process of construction in Jiangsu Province, predict the carbon emissions in the whole process of construction in Jiangsu Province in 2023.

Summary of ideas : Collect annual building carbon emission data by consulting the "2010-2021 Jiangsu Construction Industry Development Report", and then establish a time series forecasting model, ARIMA/grey forecasting/LSTM, etc. It is best to compare rmse with multiple methods and choose the most suitable of.

predictive test

Question 5: Please give policy suggestions for building carbon emission reduction in Jiangsu Province based on the previous discussion.

Idea summary : Combining the literature and the results of each question to make suggestions

For the full version of the problem-solving steps, as well as reference data, codes, and answers, please privately stamp~